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Azure Machine Learning Skill

Azure Machine Learning 技能

This skill provides expert guidance for Azure Machine Learning. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
本技能为Azure Machine Learning提供专业指导，涵盖故障排查、最佳实践、决策制定、架构与设计模式、限制与配额、安全、配置、集成与编码模式以及部署。它结合了本地快速参考内容与远程文档获取能力。

How to Use This Skill

如何使用本技能

IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g., 
L35-L120
), use 
read_file
with the specified lines. For categories with file links (e.g., 
[security.md](security.md)
), use 
read_file
on the linked reference file
IMPORTANT for Agent: If 
metadata.generated_at
is more than 3 months old, suggest the user pull the latest version from the repository. If 
mcp_microsoftdocs
tools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
  • Preferred: Use 
    mcp_microsoftdocs:microsoft_docs_fetch
    with query string 
    from=learn-agent-skill
    . Returns Markdown.
  • Fallback: Use 
    fetch_webpage
    with query string 
    from=learn-agent-skill&accept=text/markdown
    . Returns Markdown.
Agent 重要提示：使用下方的分类索引定位相关章节。对于带有行范围的分类（例如 
L35-L120
），使用
read_file
并指定对应行。对于带有文件链接的分类（例如 
[security.md](security.md)
），对链接的参考文件使用
read_file
Agent 重要提示：如果
metadata.generated_at
已超过3个月，建议用户从仓库拉取最新版本。如果
mcp_microsoftdocs
工具不可用，建议用户安装它：安装指南
本技能需要网络访问权限以获取文档内容：
  • 首选方式：使用
    mcp_microsoftdocs:microsoft_docs_fetch
    ，查询字符串为
    from=learn-agent-skill
    ，返回Markdown格式内容。
  • 备选方式：使用
    fetch_webpage
    ，查询字符串为
    from=learn-agent-skill&accept=text/markdown
    ，返回Markdown格式内容。

Category Index

分类索引

CategoryLinesDescription
TroubleshootingL37-L71Diagnosing and fixing Azure ML runtime issues: pipelines, AutoML, endpoints (online/batch), Kubernetes, networking (VNet/private), environments/images, prompt flow, and known platform issues.
Best PracticesL72-L95Best practices for ML/LLM lifecycle in Azure ML: cost, security, data ethics, feature design, training, deployment, monitoring, AutoML, prompt flow, and performance tuning.
Decision MakingL96-L119Guidance on Azure ML design choices: algorithms, training, networking, cost, DR, data labeling, and detailed migration/upgrade paths from AML v1 to v2 across jobs, data, compute, and workspaces
Architecture & Design PatternsL120-L127Designing Azure ML inference architectures: choosing endpoint types, planning real-time online endpoints, and structuring data movement and multistep pipeline components.
Limits & QuotasL128-L136Azure ML deployment limits: regional/sovereign availability, quota management, supported VM SKUs for managed endpoints, and capacity planning against service limits.
SecurityL137-L195Securing Azure ML workspaces, data, and endpoints with encryption, identity/RBAC, network isolation/VNets, private endpoints, Key Vault, and Azure Policy-based governance and compliance.
ConfigurationL196-L463Configuring Azure ML components, compute, data, monitoring, AutoML, prompt flow, and YAML/CLI settings for training, deployment, and MLOps across classic designer and v2 workflows.
Integrations & Coding PatternsL464-L507Integrating Azure ML with data sources, Spark/Databricks/Synapse, REST/MLflow, prompt flow, and external services to configure IO, logging, events, and deployment patterns.
DeploymentL508-L553Deploying and operating ML and LLM workloads in Azure ML: online/batch endpoints, MLflow, pipelines, prompt flow, CI/CD, blue‑green rollouts, and cross-workspace/model catalog deployments
分类行范围描述
故障排查L37-L71诊断并修复Azure ML运行时问题：管道、AutoML、端点（在线/批量）、Kubernetes、网络（VNet/专用）、环境/镜像、prompt flow以及已知平台问题。
最佳实践L72-L95Azure ML中ML/LLM生命周期的最佳实践：成本、安全、数据伦理、特征设计、训练、部署、监控、AutoML、prompt flow以及性能调优。
决策制定L96-L119Azure ML设计选择指导：算法、训练、网络、成本、灾难恢复、数据标注，以及从AML v1到v2在作业、数据、计算和工作区方面的详细迁移/升级路径
架构与设计模式L120-L127设计Azure ML推理架构：选择端点类型、规划实时在线端点，以及构建数据移动和多步骤管道组件的结构。
限制与配额L128-L136Azure ML部署限制：区域/主权云可用性、配额管理、托管端点支持的VM SKU，以及针对服务限制的容量规划。
安全L137-L195通过加密、身份/RBAC、网络隔离/VNet、专用端点、Key Vault以及基于Azure Policy的治理与合规，保护Azure ML工作区、数据和端点。
配置L196-L463配置Azure ML组件、计算、数据、监控、AutoML、prompt flow，以及用于训练、部署和MLOps的YAML/CLI设置，涵盖经典设计器和v2工作流。
集成与编码模式L464-L507将Azure ML与数据源、Spark/Databricks/Synapse、REST/MLflow、prompt flow和外部服务集成，以配置IO、日志、事件和部署模式。
部署L508-L553在Azure ML中部署和运行ML与LLM工作负载：在线/批量端点、MLflow、管道、prompt flow、CI/CD、蓝绿发布，以及跨工作区/模型目录部署

Troubleshooting

故障排查

TopicURL
Troubleshoot Azure ML designer component error codeshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/designer-error-codes?view=azureml-api-2
Resolve common Azure AutoML forecasting issueshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-automl-forecasting-faq?view=azureml-api-2
Debug Azure ML online endpoints locally with VS Codehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-managed-online-endpoints-visual-studio-code?view=azureml-api-2
Troubleshoot ParallelRunStep failures in Azure ML pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-parallel-run-step?view=azureml-api-1
Debug Azure ML pipeline failures in studiohttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-failure?view=azureml-api-2
Diagnose Azure ML pipeline performance issues with profilinghttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-performance?view=azureml-api-2
Debug pipeline reuse behavior in Azure Machine Learninghttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-reuse-issues?view=azureml-api-2
Troubleshoot Azure ML SDK v1 pipelines executionhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines?view=azureml-api-1
Troubleshoot Azure automated ML experiment failureshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-auto-ml?view=azureml-api-2
Troubleshoot Azure ML batch endpoints and jobshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-batch-endpoints?view=azureml-api-2
Troubleshoot data access issues in Azure ML SDK v2https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-data-access?view=azureml-api-2
Troubleshoot Azure ML data labeling project creationhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-data-labeling?view=azureml-api-2
Troubleshoot Azure ML environment image builds and packageshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-environments?view=azureml-api-2
Troubleshoot Azure ML Kubernetes compute workloadshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-kubernetes-compute?view=azureml-api-2
Troubleshoot Azure ML Kubernetes extension deploymenthttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-kubernetes-extension?view=azureml-api-2
Troubleshoot Azure ML managed virtual network issueshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-managed-network?view=azureml-api-2
Diagnose and fix Azure ML online endpoint errorshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2
Diagnose and fix Azure ML online endpoint errorshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2
Troubleshoot Azure ML online endpoint deployment and scoring errorshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2
Troubleshoot Azure ML prebuilt Docker inference imageshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-prebuilt-docker-image-inference?view=azureml-api-1
Resolve 'descriptors cannot be created directly' in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-protobuf-descriptor-error?view=azureml-api-2
Troubleshoot Azure ML private endpoint connectivityhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-secure-connection-workspace?view=azureml-api-2
Fix SerializationError import issues in Azure ML SDK v1https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-serialization-error?view=azureml-api-1
Fix 'Validation for schema failed' errors in Azure ML CLI v2https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-validation-for-schema-failed-error?view=azureml-api-2
Use Azure ML workspace diagnostics for issue analysishttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-workspace-diagnostic-api?view=azureml-api-2
Review Azure Machine Learning current known issueshttps://learn.microsoft.com/en-us/azure/machine-learning/known-issues/azure-machine-learning-known-issues?view=azureml-api-2
Known issue: Invalid certificate during AKS deploymenthttps://learn.microsoft.com/en-us/azure/machine-learning/known-issues/inferencing-invalid-certificate?view=azureml-api-2
Known issue: Updating Azure ML Kubernetes compute failshttps://learn.microsoft.com/en-us/azure/machine-learning/known-issues/inferencing-updating-kubernetes-compute-appears-to-succeed?view=azureml-api-2
Troubleshoot Azure ML prompt flow issueshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/troubleshoot-guidance?view=azureml-api-2
Troubleshoot Azure ML prompt flow issueshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/troubleshoot-guidance?view=azureml-api-2
Troubleshoot Azure ML managed feature store errorshttps://learn.microsoft.com/en-us/azure/machine-learning/troubleshooting-managed-feature-store?view=azureml-api-2
主题URL
排查Azure ML设计器组件错误代码https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/designer-error-codes?view=azureml-api-2
解决常见Azure AutoML预测问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-automl-forecasting-faq?view=azureml-api-2
使用VS Code本地调试Azure ML在线端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-managed-online-endpoints-visual-studio-code?view=azureml-api-2
排查Azure ML管道中的ParallelRunStep失败问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-parallel-run-step?view=azureml-api-1
在工作室中调试Azure ML管道失败问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-failure?view=azureml-api-2
通过分析排查Azure ML管道性能问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-performance?view=azureml-api-2
调试Azure Machine Learning中的管道重用行为https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-reuse-issues?view=azureml-api-2
排查Azure ML SDK v1管道执行问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines?view=azureml-api-1
排查Azure自动化ML实验失败问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-auto-ml?view=azureml-api-2
排查Azure ML批量端点和作业问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-batch-endpoints?view=azureml-api-2
排查Azure ML SDK v2中的数据访问问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-data-access?view=azureml-api-2
排查Azure ML数据标注项目创建问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-data-labeling?view=azureml-api-2
排查Azure ML环境镜像构建和包问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-environments?view=azureml-api-2
排查Azure ML Kubernetes计算工作负载问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-kubernetes-compute?view=azureml-api-2
排查Azure ML Kubernetes扩展部署问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-kubernetes-extension?view=azureml-api-2
排查Azure ML托管虚拟网络问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-managed-network?view=azureml-api-2
诊断并修复Azure ML在线端点错误https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2
诊断并修复Azure ML在线端点错误https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2
排查Azure ML在线端点部署和评分错误https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2
排查Azure ML预构建Docker推理镜像问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-prebuilt-docker-image-inference?view=azureml-api-1
解决Azure ML中的“descriptors cannot be created directly”问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-protobuf-descriptor-error?view=azureml-api-2
排查Azure ML专用端点连接问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-secure-connection-workspace?view=azureml-api-2
修复Azure ML SDK v1中的SerializationError导入问题https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-serialization-error?view=azureml-api-1
修复Azure ML CLI v2中的“Validation for schema failed”错误https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-validation-for-schema-failed-error?view=azureml-api-2
使用Azure ML工作区诊断进行问题分析https://learn.microsoft.com/en-us/azure/machine-learning/how-to-workspace-diagnostic-api?view=azureml-api-2
查看Azure Machine Learning当前已知问题https://learn.microsoft.com/en-us/azure/machine-learning/known-issues/azure-machine-learning-known-issues?view=azureml-api-2
已知问题：AKS部署期间的无效证书https://learn.microsoft.com/en-us/azure/machine-learning/known-issues/inferencing-invalid-certificate?view=azureml-api-2
已知问题：更新Azure ML Kubernetes计算失败https://learn.microsoft.com/en-us/azure/machine-learning/known-issues/inferencing-updating-kubernetes-compute-appears-to-succeed?view=azureml-api-2
排查Azure ML prompt flow问题https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/troubleshoot-guidance?view=azureml-api-2
排查Azure ML prompt flow问题https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/troubleshoot-guidance?view=azureml-api-2
排查Azure ML托管特征存储错误https://learn.microsoft.com/en-us/azure/machine-learning/troubleshooting-managed-feature-store?view=azureml-api-2

Best Practices

最佳实践

TopicURL
Mitigate overfitting and imbalance in Azure AutoMLhttps://learn.microsoft.com/en-us/azure/machine-learning/concept-manage-ml-pitfalls?view=azureml-api-2
Understand Azure ML model monitoring concepts and practiceshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-model-monitoring?view=azureml-api-2
Optimize and manage Azure Machine Learning costshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-plan-manage-cost?view=azureml-api-2
Apply secure coding practices in Azure ML notebookshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-code-best-practice?view=azureml-api-2
Ethical best practices for sourcing human datahttps://learn.microsoft.com/en-us/azure/machine-learning/concept-sourcing-human-data?view=azureml-api-2
Design feature set transformations in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/feature-set-specification-transformation-concepts?view=azureml-api-2
Author batch scoring scripts for AML batch deploymentshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-batch-scoring-script?view=azureml-api-2
Write advanced Azure ML entry scripts for inferencehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-advanced-entry-script?view=azureml-api-1
Profile AML model CPU and memory usage before deploymenthttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-profile-model?view=azureml-api-1
Tune Azure ML Kubernetes inference router performancehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-kubernetes-inference-routing-azureml-fe?view=azureml-api-2
Manage Azure ML compute notebook and terminal sessionshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-compute-sessions?view=azureml-api-2
Optimize Azure Machine Learning compute costshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-optimize-cost?view=azureml-api-2
Choose storage locations for Azure ML experiment fileshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-save-write-experiment-files?view=azureml-api-1
Apply best practices for distributed GPU training in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-distributed-gpu?view=azureml-api-2
Evaluate and compare Azure AutoML experiment resultshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml?view=azureml-api-2
Optimize AutoML for small object detection in imageshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automl-small-object-detect?view=azureml-api-2
Apply generative AI monitoring metrics and recommended practices in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring-generative-ai-evaluation-metrics?view=azureml-api-2
Design and use evaluation flows and metrics in prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-develop-an-evaluation-flow?view=azureml-api-2
Tune LLM prompts using variants in Azure ML prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-tune-prompts-using-variants?view=azureml-api-2
Optimize checkpoint performance for large Azure ML models with Nebulahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-checkpoint-performance-for-large-models?view=azureml-api-2
主题URL
缓解Azure AutoML中的过拟合和数据不平衡问题https://learn.microsoft.com/en-us/azure/machine-learning/concept-manage-ml-pitfalls?view=azureml-api-2
了解Azure ML模型监控概念与实践https://learn.microsoft.com/en-us/azure/machine-learning/concept-model-monitoring?view=azureml-api-2
优化和管理Azure Machine Learning成本https://learn.microsoft.com/en-us/azure/machine-learning/concept-plan-manage-cost?view=azureml-api-2
在Azure ML笔记本中应用安全编码实践https://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-code-best-practice?view=azureml-api-2
人工数据获取的伦理最佳实践https://learn.microsoft.com/en-us/azure/machine-learning/concept-sourcing-human-data?view=azureml-api-2
在Azure ML中设计特征集转换https://learn.microsoft.com/en-us/azure/machine-learning/feature-set-specification-transformation-concepts?view=azureml-api-2
为AML批量部署编写批量评分脚本https://learn.microsoft.com/en-us/azure/machine-learning/how-to-batch-scoring-script?view=azureml-api-2
编写Azure ML推理用高级入口脚本https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-advanced-entry-script?view=azureml-api-1
部署前分析AML模型的CPU和内存使用情况https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-profile-model?view=azureml-api-1
调优Azure ML Kubernetes推理路由器性能https://learn.microsoft.com/en-us/azure/machine-learning/how-to-kubernetes-inference-routing-azureml-fe?view=azureml-api-2
管理Azure ML计算笔记本和终端会话https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-compute-sessions?view=azureml-api-2
优化Azure Machine Learning计算成本https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-optimize-cost?view=azureml-api-2
为Azure ML实验文件选择存储位置https://learn.microsoft.com/en-us/azure/machine-learning/how-to-save-write-experiment-files?view=azureml-api-1
在Azure ML中应用分布式GPU训练最佳实践https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-distributed-gpu?view=azureml-api-2
评估和比较Azure AutoML实验结果https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml?view=azureml-api-2
优化AutoML以进行图像小目标检测https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automl-small-object-detect?view=azureml-api-2
在Azure ML中应用生成式AI监控指标和推荐实践https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring-generative-ai-evaluation-metrics?view=azureml-api-2
在prompt flow中设计和使用评估流与指标https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-develop-an-evaluation-flow?view=azureml-api-2
使用Azure ML prompt flow中的变体调优LLM提示词https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-tune-prompts-using-variants?view=azureml-api-2
使用Nebula优化Azure ML大模型的检查点性能https://learn.microsoft.com/en-us/azure/machine-learning/reference-checkpoint-performance-for-large-models?view=azureml-api-2

Decision Making

决策制定

TopicURL
Choose Azure ML designer algorithms with cheat sheethttps://learn.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet?view=azureml-api-1
Plan Azure ML registries for multi-environment MLOpshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-machine-learning-registries-mlops?view=azureml-api-2
Choose between managed and custom network isolation in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/concept-network-isolation-configurations?view=azureml-api-2
Choose the right Azure ML training methodhttps://learn.microsoft.com/en-us/azure/machine-learning/concept-train-machine-learning-model?view=azureml-api-2
Choose migration paths from Azure ML Data Import to Fabrichttps://learn.microsoft.com/en-us/azure/machine-learning/data-import-migration-guide?view=azureml-api-2
Plan failover and disaster recovery for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-high-availability-machine-learning?view=azureml-api-2
Decide when and how to upgrade AML v1 to v2https://learn.microsoft.com/en-us/azure/machine-learning/how-to-migrate-from-v1?view=azureml-api-2
Move Azure ML workspaces between subscriptionshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-move-workspace?view=azureml-api-2
Plan Azure ML network isolation architecturehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-isolation-planning?view=azureml-api-2
Use vendor companies for Azure ML data labelinghttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-outsource-data-labeling?view=azureml-api-2
Select appropriate Azure ML algorithms for taskshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-select-algorithms?view=azureml-api-1
Use low-priority VMs for AML batch inference cost savingshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch?view=azureml-api-2
Map AML v1 datasets to v2 data assetshttps://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-assets-data?view=azureml-api-2
Upgrade model management workflows from AML v1 to v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-assets-model?view=azureml-api-2
Migrate script run jobs to AML SDK v2 command jobshttps://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-command-job?view=azureml-api-2
Upgrade AutoML configurations from AML SDK v1 to v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-automl?view=azureml-api-2
Compare local run workflows between AML v1 and v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-local-runs?view=azureml-api-2
Evaluate compute management changes from AML v1 to v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-compute?view=azureml-api-2
Migrate datastore management from AML v1 to v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-datastore?view=azureml-api-2
Compare workspace management between AML SDK v1 and v2https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-workspace?view=azureml-api-2
主题URL
使用速查表选择Azure ML设计器算法https://learn.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet?view=azureml-api-1
为多环境MLOps规划Azure ML注册表https://learn.microsoft.com/en-us/azure/machine-learning/concept-machine-learning-registries-mlops?view=azureml-api-2
在Azure ML中选择托管与自定义网络隔离https://learn.microsoft.com/en-us/azure/machine-learning/concept-network-isolation-configurations?view=azureml-api-2
选择合适的Azure ML训练方法https://learn.microsoft.com/en-us/azure/machine-learning/concept-train-machine-learning-model?view=azureml-api-2
选择从Azure ML数据导入到Fabric的迁移路径https://learn.microsoft.com/en-us/azure/machine-learning/data-import-migration-guide?view=azureml-api-2
规划Azure ML的故障转移和灾难恢复https://learn.microsoft.com/en-us/azure/machine-learning/how-to-high-availability-machine-learning?view=azureml-api-2
决定何时以及如何从AML v1升级到v2https://learn.microsoft.com/en-us/azure/machine-learning/how-to-migrate-from-v1?view=azureml-api-2
在订阅之间移动Azure ML工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-move-workspace?view=azureml-api-2
规划Azure ML网络隔离架构https://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-isolation-planning?view=azureml-api-2
使用供应商公司进行Azure ML数据标注https://learn.microsoft.com/en-us/azure/machine-learning/how-to-outsource-data-labeling?view=azureml-api-2
为任务选择合适的Azure ML算法https://learn.microsoft.com/en-us/azure/machine-learning/how-to-select-algorithms?view=azureml-api-1
使用低优先级VM降低AML批量推理成本https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch?view=azureml-api-2
将AML v1数据集映射到v2数据资产https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-assets-data?view=azureml-api-2
从AML v1升级到v2的模型管理工作流https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-assets-model?view=azureml-api-2
将脚本运行作业迁移到AML SDK v2命令作业https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-command-job?view=azureml-api-2
从AML SDK v1升级到v2的AutoML配置https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-automl?view=azureml-api-2
比较AML v1和v2之间的本地运行工作流https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-local-runs?view=azureml-api-2
评估从AML v1到v2的计算管理变化https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-compute?view=azureml-api-2
从AML v1迁移到v2的数据存储管理https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-datastore?view=azureml-api-2
比较AML SDK v1和v2之间的工作区管理https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-workspace?view=azureml-api-2

Architecture & Design Patterns

架构与设计模式

TopicURL
Plan real-time inference with Azure ML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints-online?view=azureml-api-2
Understand Azure ML endpoint types for inferencehttps://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints?view=azureml-api-2
Design data movement patterns in Azure ML pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-move-data-in-out-of-pipelines?view=azureml-api-1
Design multistep pipeline components in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-pipeline-component?view=azureml-api-2
主题URL
使用Azure ML在线端点规划实时推理https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints-online?view=azureml-api-2
了解用于推理的Azure ML端点类型https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints?view=azureml-api-2
在Azure ML管道中设计数据移动模式https://learn.microsoft.com/en-us/azure/machine-learning/how-to-move-data-in-out-of-pipelines?view=azureml-api-1
在Azure ML中设计多步骤管道组件https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-pipeline-component?view=azureml-api-2

Limits & Quotas

限制与配额

TopicURL
Check regional availability for Azure ML standard deploymentshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoint-serverless-availability?view=azureml-api-2
Manage Azure ML resource quotas and limitshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas?view=azureml-api-2
Check Azure ML feature availability by sovereign cloudhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-machine-learning-cloud-parity?view=azureml-api-2
Supported VM SKUs for Azure ML managed online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/reference-managed-online-endpoints-vm-sku-list?view=azureml-api-2
Plan capacity with Azure Machine Learning service limitshttps://learn.microsoft.com/en-us/azure/machine-learning/resource-limits-capacity?view=azureml-api-2
主题URL
检查Azure ML标准部署的区域可用性https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoint-serverless-availability?view=azureml-api-2
管理Azure ML资源配额和限制https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas?view=azureml-api-2
按主权云检查Azure ML功能可用性https://learn.microsoft.com/en-us/azure/machine-learning/reference-machine-learning-cloud-parity?view=azureml-api-2
Azure ML托管在线端点支持的VM SKUhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-managed-online-endpoints-vm-sku-list?view=azureml-api-2
根据Azure Machine Learning服务限制规划容量https://learn.microsoft.com/en-us/azure/machine-learning/resource-limits-capacity?view=azureml-api-2

Security

安全

TopicURL
Use customer-managed keys with Azure Machine Learninghttps://learn.microsoft.com/en-us/azure/machine-learning/concept-customer-managed-keys?view=azureml-api-2
Implement data encryption for Azure ML storage and computehttps://learn.microsoft.com/en-us/azure/machine-learning/concept-data-encryption?view=azureml-api-2
Understand data handling and privacy for Model Catalog deploymentshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-data-privacy?view=azureml-api-2
Understand auth and RBAC for AML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints-online-auth?view=azureml-api-2
Plan enterprise security and governance for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/concept-enterprise-security?view=azureml-api-2
Secret injection concepts for AML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-secret-injection?view=azureml-api-2
Understand secure network traffic flow in Azure ML VNetshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-network-traffic-flow?view=azureml-api-2
Network isolation concepts for AML managed endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-online-endpoint?view=azureml-api-2
Manage vulnerabilities for Azure ML images and componentshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-vulnerability-management?view=azureml-api-2
Configure inbound and outbound network traffic for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-azureml-behind-firewall?view=azureml-api-2
Securely access on-premises resources from Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-on-premises-resources?view=azureml-api-2
Access Azure resources from AML endpoints via managed identityhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-resources-from-endpoints-managed-identities?view=azureml-api-2
Grant limited access to Azure ML labeling projectshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-add-users?view=azureml-api-2
Administer data access and authentication for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-administrate-data-authentication?view=azureml-api-2
Configure data authentication for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-administrate-data-authentication?view=azureml-api-2
Manage Azure RBAC roles for Azure ML workspaceshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles?view=azureml-api-2
Authenticate and authorize access to AML batch endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-batch-endpoint?view=azureml-api-2
Authenticate clients to Azure ML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-online-endpoint?view=azureml-api-2
Configure authentication for Azure ML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-online-endpoint?view=azureml-api-2
Configure authentication for Azure ML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-online-endpoint?view=azureml-api-2
Use built-in Azure Policy to govern AI model deploymentshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-built-in-policy-model-deployment?view=azureml-api-2
Rotate Azure ML workspace storage account access keyshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-change-storage-access-key?view=azureml-api-2
Maintain network isolation with Azure ML v2 ARM APIshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-network-isolation-with-v2?view=azureml-api-2
Configure private endpoints for Azure ML workspaceshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-private-link?view=azureml-api-2
Create custom Azure Policies to restrict AI model deploymentshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-custom-policy-model-deployment?view=azureml-api-2
Use secret injection to access secrets in AML deploymentshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoint-with-secret-injection?view=azureml-api-2
Disable shared key access for Azure ML workspace storagehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-disable-local-auth-storage?view=azureml-api-2
Enable Azure ML studio access inside virtual networkshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-enable-studio-virtual-network?view=azureml-api-2
Configure identity-based service authentication for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication?view=azureml-api-2
Configure identity-based service authentication for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication?view=azureml-api-2
Enforce Azure ML workspace compliance with Azure Policyhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-integrate-azure-policy?view=azureml-api-2
Configure Azure ML managed virtual network isolationhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-managed-network?view=azureml-api-2
Configure Model Catalog access with workspace managed virtual networkshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-isolation-model-catalog?view=azureml-api-2
Secure Azure ML workspaces with virtual networkshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-security-overview?view=azureml-api-2
Configure data exfiltration prevention for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-prevent-data-loss-exfiltration?view=azureml-api-2
Isolate Azure ML registries with VNets and private endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-registry-network-isolation?view=azureml-api-2
Configure network isolation for AML batch endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-batch-endpoint?view=azureml-api-2
Secure Azure ML online inferencing with VNetshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-inferencing-vnet?view=azureml-api-2
Secure AKS inferencing environments for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-kubernetes-inferencing-environment?view=azureml-api-2
Configure TLS/SSL for Azure ML Kubernetes endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-kubernetes-online-endpoint?view=azureml-api-2
Secure Azure ML managed online endpoints with network isolationhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-online-endpoint?view=azureml-api-2
Secure Azure ML RAG workflows with network isolationhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-rag-workflows?view=azureml-api-2
Secure Azure ML training environments with VNetshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-training-vnet?view=azureml-api-2
Secure Azure ML workspace using virtual networkshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-workspace-vnet?view=azureml-api-2
Configure RBAC access to Azure ML feature storehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-access-control-feature-store?view=azureml-api-2
Set up authentication to Azure ML workspaceshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-authentication?view=azureml-api-2
Configure customer-managed keys for Azure ML resourceshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-customer-managed-keys?view=azureml-api-2
Securely use private Python packages in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-private-python-packages?view=azureml-api-1
Securely use Key Vault secrets in Azure ML runshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-secrets-in-runs?view=azureml-api-2
Apply built-in Azure Policy definitions for AMLhttps://learn.microsoft.com/en-us/azure/machine-learning/policy-reference?view=azureml-api-2
Manage API and data source credentials with prompt flow connectionshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-connections?view=azureml-api-2
Secure prompt flow with virtual network isolation in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-secure-prompt-flow?view=azureml-api-2
Apply Azure Policy regulatory controls to Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/security-controls-policy?view=azureml-api-2
Secure Azure ML workspace with custom VNethttps://learn.microsoft.com/en-us/azure/machine-learning/tutorial-create-secure-workspace-vnet?view=azureml-api-2
Create a secure Azure ML workspace with managed VNethttps://learn.microsoft.com/en-us/azure/machine-learning/tutorial-create-secure-workspace?view=azureml-api-2
主题URL
在Azure Machine Learning中使用客户管理密钥https://learn.microsoft.com/en-us/azure/machine-learning/concept-customer-managed-keys?view=azureml-api-2
为Azure ML存储和计算实施数据加密https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-encryption?view=azureml-api-2
了解模型目录部署的数据处理与隐私https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-privacy?view=azureml-api-2
了解AML在线端点的身份验证和RBAChttps://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints-online-auth?view=azureml-api-2
规划Azure ML的企业安全与治理https://learn.microsoft.com/en-us/azure/machine-learning/concept-enterprise-security?view=azureml-api-2
AML在线端点的密钥注入概念https://learn.microsoft.com/en-us/azure/machine-learning/concept-secret-injection?view=azureml-api-2
了解Azure ML VNet中的安全网络流量流https://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-network-traffic-flow?view=azureml-api-2
AML托管端点的网络隔离概念https://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-online-endpoint?view=azureml-api-2
管理Azure ML镜像和组件的漏洞https://learn.microsoft.com/en-us/azure/machine-learning/concept-vulnerability-management?view=azureml-api-2
配置Azure ML的入站和出站网络流量https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-azureml-behind-firewall?view=azureml-api-2
从Azure ML安全访问本地资源https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-on-premises-resources?view=azureml-api-2
通过托管身份从AML端点访问Azure资源https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-resources-from-endpoints-managed-identities?view=azureml-api-2
为Azure ML标注项目授予有限访问权限https://learn.microsoft.com/en-us/azure/machine-learning/how-to-add-users?view=azureml-api-2
管理Azure ML的数据访问和身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-administrate-data-authentication?view=azureml-api-2
配置Azure ML的数据身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-administrate-data-authentication?view=azureml-api-2
为Azure ML工作区管理Azure RBAC角色https://learn.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles?view=azureml-api-2
对AML批量端点进行身份验证和授权https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-batch-endpoint?view=azureml-api-2
对Azure ML在线端点的客户端进行身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-online-endpoint?view=azureml-api-2
配置Azure ML在线端点的身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-online-endpoint?view=azureml-api-2
配置Azure ML在线端点的身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-online-endpoint?view=azureml-api-2
使用内置Azure Policy治理AI模型部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-built-in-policy-model-deployment?view=azureml-api-2
轮换Azure ML工作区存储账户访问密钥https://learn.microsoft.com/en-us/azure/machine-learning/how-to-change-storage-access-key?view=azureml-api-2
使用Azure ML v2 ARM API维护网络隔离https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-network-isolation-with-v2?view=azureml-api-2
为Azure ML工作区配置专用端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-private-link?view=azureml-api-2
创建自定义Azure Policy以限制AI模型部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-custom-policy-model-deployment?view=azureml-api-2
使用密钥注入访问AML部署中的密钥https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoint-with-secret-injection?view=azureml-api-2
禁用Azure ML工作区存储的共享密钥访问https://learn.microsoft.com/en-us/azure/machine-learning/how-to-disable-local-auth-storage?view=azureml-api-2
在虚拟网络中启用Azure ML工作室访问https://learn.microsoft.com/en-us/azure/machine-learning/how-to-enable-studio-virtual-network?view=azureml-api-2
为Azure ML配置基于身份的服务身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication?view=azureml-api-2
为Azure ML配置基于身份的服务身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication?view=azureml-api-2
使用Azure Policy强制Azure ML工作区合规https://learn.microsoft.com/en-us/azure/machine-learning/how-to-integrate-azure-policy?view=azureml-api-2
配置Azure ML托管虚拟网络隔离https://learn.microsoft.com/en-us/azure/machine-learning/how-to-managed-network?view=azureml-api-2
使用工作区托管虚拟网络配置模型目录访问https://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-isolation-model-catalog?view=azureml-api-2
使用虚拟网络保护Azure ML工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-security-overview?view=azureml-api-2
为Azure ML配置数据泄露防护https://learn.microsoft.com/en-us/azure/machine-learning/how-to-prevent-data-loss-exfiltration?view=azureml-api-2
使用VNet和专用端点隔离Azure ML注册表https://learn.microsoft.com/en-us/azure/machine-learning/how-to-registry-network-isolation?view=azureml-api-2
为AML批量端点配置网络隔离https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-batch-endpoint?view=azureml-api-2
使用VNet保护Azure ML在线推理https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-inferencing-vnet?view=azureml-api-2
为Azure ML保护AKS推理环境https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-kubernetes-inferencing-environment?view=azureml-api-2
为Azure ML Kubernetes端点配置TLS/SSLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-kubernetes-online-endpoint?view=azureml-api-2
使用网络隔离保护Azure ML托管在线端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-online-endpoint?view=azureml-api-2
使用网络隔离保护Azure ML RAG工作流https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-rag-workflows?view=azureml-api-2
使用VNet保护Azure ML训练环境https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-training-vnet?view=azureml-api-2
使用虚拟网络保护Azure ML工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-workspace-vnet?view=azureml-api-2
为Azure ML特征存储配置RBAC访问https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-access-control-feature-store?view=azureml-api-2
设置对Azure ML工作区的身份验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-authentication?view=azureml-api-2
为Azure ML资源配置客户管理密钥https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-customer-managed-keys?view=azureml-api-2
在Azure ML中安全使用私有Python包https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-private-python-packages?view=azureml-api-1
在Azure ML运行中安全使用Key Vault密钥https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-secrets-in-runs?view=azureml-api-2
为AML应用内置Azure Policy定义https://learn.microsoft.com/en-us/azure/machine-learning/policy-reference?view=azureml-api-2
使用prompt flow连接管理API和数据源凭据https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-connections?view=azureml-api-2
在Azure ML中使用虚拟网络隔离保护prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-secure-prompt-flow?view=azureml-api-2
为Azure ML应用Azure Policy法规控制https://learn.microsoft.com/en-us/azure/machine-learning/security-controls-policy?view=azureml-api-2
使用自定义VNet保护Azure ML工作区https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-create-secure-workspace-vnet?view=azureml-api-2
使用托管VNet创建安全的Azure ML工作区https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-create-secure-workspace?view=azureml-api-2

Configuration

配置

TopicURL
Configure AutoML Classification component with ML Tableshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/classification?view=azureml-api-2
Configure AutoML Forecasting component in designerhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/forecasting?view=azureml-api-2
Configure AutoML Image Multi-label Classificationhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-classification-multilabel?view=azureml-api-2
Configure AutoML Image Classification componenthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-classification?view=azureml-api-2
Configure AutoML Image Instance Segmentation componenthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-instance-segmentation?view=azureml-api-2
Configure AutoML Image Object Detection componenthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-object-detection?view=azureml-api-2
Configure AutoML Regression component with ML Tableshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/regression?view=azureml-api-2
Configure AutoML Text Multi-label Classification componenthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-classification-multilabel?view=azureml-api-2
Configure AutoML Text Classification componenthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-classification?view=azureml-api-2
Configure AutoML Text NER component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-ner?view=azureml-api-2
Configure Add Columns component to concatenate datasetshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/add-columns?view=azureml-api-2
Configure Add Rows component to append dataset recordshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/add-rows?view=azureml-api-2
Configure Apply Image Transformation in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-image-transformation?view=azureml-api-2
Configure Apply Math Operation component for column calculationshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-math-operation?view=azureml-api-2
Configure Apply SQL Transformation component using SQLitehttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-sql-transformation?view=azureml-api-2
Configure Apply Transformation component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-transformation?view=azureml-api-2
Configure Assign Data to Clusters in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/assign-data-to-clusters?view=azureml-api-2
Configure Boosted Decision Tree Regression component (LightGBM)https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/boosted-decision-tree-regression?view=azureml-api-2
Configure Clean Missing Data component for handling nullshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/clean-missing-data?view=azureml-api-2
Configure Clip Values component to handle outliershttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/clip-values?view=azureml-api-2
Configure and use Azure ML designer algorithm componentshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/component-reference?view=azureml-api-2
Configure Convert to CSV component for dataset exporthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-csv?view=azureml-api-2
Configure Convert to Dataset component for internal formathttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-dataset?view=azureml-api-2
Configure Convert to Image Directory in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-image-directory?view=azureml-api-2
Configure Convert to Indicator Values for categorical encodinghttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-indicator-values?view=azureml-api-2
Configure Convert Word to Vector component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-word-to-vector?view=azureml-api-2
Configure Create Python Model component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/create-python-model?view=azureml-api-2
Configure Cross Validate Model component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/cross-validate-model?view=azureml-api-2
Configure Decision Forest Regression in Azure ML designerhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/decision-forest-regression?view=azureml-api-2
Configure DenseNet image classification componenthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/densenet?view=azureml-api-2
Configure Edit Metadata component to adjust column roleshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/edit-metadata?view=azureml-api-2
Set up Enter Data Manually component for small datasetshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/enter-data-manually?view=azureml-api-2
Configure Evaluate Model component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-model?view=azureml-api-2
Configure Evaluate Recommender component for model accuracyhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-recommender?view=azureml-api-2
Configure Execute Python Script in Azure ML designerhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/execute-python-script?view=azureml-api-2
Configure Execute R Script component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/execute-r-script?view=azureml-api-2
Configure Export Data component to save pipeline outputshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/export-data?view=azureml-api-2
Configure Extract N-Gram Features from Text in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/extract-n-gram-features-from-text?view=azureml-api-2
Configure Fast Forest Quantile Regression in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/fast-forest-quantile-regression?view=azureml-api-2
Configure Feature Hashing text component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/feature-hashing?view=azureml-api-2
Configure Filter Based Feature Selection for predictive columnshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/filter-based-feature-selection?view=azureml-api-2
Use graph search query syntax in Azure ML designerhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/graph-search-syntax?view=azureml-api-2
Configure Group Data into Bins component for discretizationhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/group-data-into-bins?view=azureml-api-2
Configure Import Data component for Azure ML designer pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/import-data?view=azureml-api-2
Configure Init Image Transformation in Azure ML designerhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/init-image-transformation?view=azureml-api-2
Configure Join Data component to merge datasetshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/join-data?view=azureml-api-2
Configure K-Means Clustering component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/k-means-clustering?view=azureml-api-2
Configure Latent Dirichlet Allocation component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/latent-dirichlet-allocation?view=azureml-api-2
Configure Linear Regression component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/linear-regression?view=azureml-api-2
Configure Multiclass Boosted Decision Tree in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-boosted-decision-tree?view=azureml-api-2
Configure Multiclass Decision Forest in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-decision-forest?view=azureml-api-2
Configure Multiclass Logistic Regression in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-logistic-regression?view=azureml-api-2
Configure Multiclass Neural Network in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-neural-network?view=azureml-api-2
Set up Neural Network Regression in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/neural-network-regression?view=azureml-api-2
Configure Normalize Data component for feature scalinghttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/normalize-data?view=azureml-api-2
Configure One-vs-All Multiclass component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/one-vs-all-multiclass?view=azureml-api-2
Configure One-vs-One Multiclass component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/one-vs-one-multiclass?view=azureml-api-2
Configure Partition and Sample component for dataset splittinghttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/partition-and-sample?view=azureml-api-2
Configure deprecated PCA-Based Anomaly Detection componenthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/pca-based-anomaly-detection?view=azureml-api-2
Configure Permutation Feature Importance component for model insightshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/permutation-feature-importance?view=azureml-api-2
Use Poisson Regression component in Azure ML designerhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/poisson-regression?view=azureml-api-2
Configure Preprocess Text component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/preprocess-text?view=azureml-api-2
Configure Remove Duplicate Rows component for deduplicationhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/remove-duplicate-rows?view=azureml-api-2
Configure ResNet image classification in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/resnet?view=azureml-api-2
Configure Score Image Model component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-image-model?view=azureml-api-2
Configure Score Model component in Azure ML designerhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-model?view=azureml-api-2
Configure Score SVD Recommender for predictionshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-svd-recommender?view=azureml-api-2
Configure Score Vowpal Wabbit Model in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-vowpal-wabbit-model?view=azureml-api-2
Configure Score Wide & Deep Recommender componenthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-wide-and-deep-recommender?view=azureml-api-2
Configure Select Columns in Dataset to subset featureshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/select-columns-in-dataset?view=azureml-api-2
Configure Select Columns Transform for stable feature setshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/select-columns-transform?view=azureml-api-2
Configure SMOTE component to oversample minority classeshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/smote?view=azureml-api-2
Configure Split Data component for train-test partitioninghttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/split-data?view=azureml-api-2
Configure Split Image Directory component for datasetshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/split-image-directory?view=azureml-api-2
Configure Summarize Data component for descriptive statisticshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/summarize-data?view=azureml-api-2
Configure Train Anomaly Detection Model componenthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-anomaly-detection-model?view=azureml-api-2
Configure Train Clustering Model component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-clustering-model?view=azureml-api-2
Configure Train PyTorch Model component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-pytorch-model?view=azureml-api-2
Configure Train SVD Recommender in Azure ML designerhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-svd-recommender?view=azureml-api-2
Configure Train Vowpal Wabbit Model in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-vowpal-wabbit-model?view=azureml-api-2
Configure Train Wide & Deep Recommender componenthttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-wide-and-deep-recommender?view=azureml-api-2
Configure Tune Model Hyperparameters in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/tune-model-hyperparameters?view=azureml-api-2
Configure Two-Class Averaged Perceptron in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?view=azureml-api-2
Configure Two-Class Boosted Decision Tree in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-boosted-decision-tree?view=azureml-api-2
Configure Two-Class Decision Forest in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-decision-forest?view=azureml-api-2
Configure Two-Class Logistic Regression in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-logistic-regression?view=azureml-api-2
Configure Two-Class Neural Network in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-neural-network?view=azureml-api-2
Configure Two-Class SVM component in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-support-vector-machine?view=azureml-api-2
Configure Web Service Input and Output componentshttps://learn.microsoft.com/en-us/azure/machine-learning/component-reference/web-service-input-output?view=azureml-api-2
Use expressions in Azure ML SDK and CLI v2 jobshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-expressions?view=azureml-api-2
Specify models for Azure ML online deploymentshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-online-deployment-model-specification?view=azureml-api-2
Use Azure ML prebuilt Docker images for inferencehttps://learn.microsoft.com/en-us/azure/machine-learning/concept-prebuilt-docker-images-inference?view=azureml-api-2
Configure and use Azure ML Responsible AI dashboardhttps://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard?view=azureml-api-2
Use workspace soft delete and recovery in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/concept-soft-delete?view=azureml-api-2
Configure Git integration for Azure ML training jobshttps://learn.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration?view=azureml-api-2
Configure and use vector stores in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/concept-vector-stores?view=azureml-api-2
Link OneLake tables to Azure ML via datastore UIhttps://learn.microsoft.com/en-us/azure/machine-learning/create-datastore-with-user-interface?view=azureml-api-2
Configure feature retrieval specs for training and inferencehttps://learn.microsoft.com/en-us/azure/machine-learning/feature-retrieval-concepts?view=azureml-api-2
Configure feature set materialization in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/feature-set-materialization-concepts?view=azureml-api-2
Access Azure cloud storage data during interactive ML developmenthttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-data-interactive?view=azureml-api-2
Configure Kubernetes compute targets for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2
Configure Kubernetes compute targets for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2
Configure Kubernetes compute targets for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2
Attach Kubernetes clusters to Azure ML workspaceshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-to-workspace?view=azureml-api-2
Configure Azure AutoML for time-series forecastinghttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-forecast?view=azureml-api-2
Configure AutoML computer vision training in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?view=azureml-api-2
Configure Azure AutoML for custom NLP traininghttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-nlp-models?view=azureml-api-2
Configure autoscaling for Azure ML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-autoscale-endpoints?view=azureml-api-2
Configure custom Azure Container for PyTorch environmentshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-azure-container-for-pytorch-environment?view=azureml-api-2
Enable production inference data collection for Azure ML endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-collect-production-data?view=azureml-api-2
Customize AutoML data featurization settings in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-features?view=azureml-api-1
Configure Azure AutoML tabular training with SDK v2https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train?view=azureml-api-2
Configure data splits and cross-validation in Azure AutoMLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits?view=azureml-api-1
Configure Azure ML connections to external data and serviceshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-connection?view=azureml-api-2
Create and manage Azure ML compute clustershttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?view=azureml-api-2
Configure and manage Azure ML compute in studiohttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio?view=azureml-api-2
Create Azure ML compute instances for developmenthttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-compute-instance?view=azureml-api-2
Create Azure ML compute instances for developmenthttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-compute-instance?view=azureml-api-2
Create and manage Azure ML data assetshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets?view=azureml-api-2
Create and manage Azure ML data assetshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets?view=azureml-api-2
Configure image labeling projects in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-image-labeling-projects?view=azureml-api-2
Configure text labeling projects in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-text-labeling-projects?view=azureml-api-2
Create and configure vector indexes in Azure ML prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-vector-index?view=azureml-api-2
Create Azure ML workspaces with ARM templateshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template?view=azureml-api-2
Configure custom DNS for Azure ML private endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-custom-dns?view=azureml-api-2
Customize Azure ML compute instances with startup scriptshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-customize-compute-instance?view=azureml-api-2
Configure and use Azure ML datastores for storage accesshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2
Deploy Azure ML extension on Kubernetes clustershttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-kubernetes-extension?view=azureml-api-2
Export or delete Azure ML workspace datahttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-export-delete-data?view=azureml-api-2
Customize Azure ML prebuilt Docker images for inferencehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-extend-prebuilt-docker-image-inference?view=azureml-api-1
Import external data into Azure ML as data assetshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-import-data-assets?view=azureml-api-2
Label images and text in Azure ML projectshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-label-data?view=azureml-api-2
Link Synapse and Azure ML workspaces with Spark poolshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-link-synapse-ml-workspaces?view=azureml-api-1
Log MLflow models as first-class models in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-mlflow-models?view=azureml-api-2
Send Azure ML distributed training logs to Application Insightshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-search?view=azureml-api-2
Configure model interpretability in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability?view=azureml-api-2
Manage Azure ML compute instances and lifecyclehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-compute-instance?view=azureml-api-2
Configure Azure ML environments via CLI and SDKhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2
Manage Azure ML environments via CLI and SDKhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2
Create Azure ML hub workspaces with Bicep templateshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-hub-workspace-template?view=azureml-api-2
Manage lifecycle and auto-delete for imported data assetshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-imported-data-assets?view=azureml-api-2
Manage component and pipeline inputs/outputs in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-inputs-outputs-pipeline?view=azureml-api-2
Create and manage Azure ML Kubernetes instance typeshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-kubernetes-instance-types?view=azureml-api-2
Administer and export Azure ML labeling projectshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-labeling-projects?view=azureml-api-2
Manage Azure ML model registry using MLflowhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models-mlflow?view=azureml-api-2
Register and manage models with Azure ML CLI and SDKhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models?view=azureml-api-2
Create and manage Azure ML registrieshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-registries?view=azureml-api-2
Manage Azure ML resources using the VS Code extensionhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-resources-vscode?view=azureml-api-2
Attach and manage Synapse Spark pools in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-synapse-spark-pool?view=azureml-api-2
Provision Azure ML workspaces using Terraformhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-terraform?view=azureml-api-2
Configure data drift monitors in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets?view=azureml-api-1
Collect and monitor Kubernetes endpoint inference logshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-kubernetes-online-enpoint-inference-server-log?view=azureml-api-2
Configure Azure ML model performance monitoring in productionhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-model-performance?view=azureml-api-2
Configure monitoring and logging for Azure ML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-online-endpoints?view=azureml-api-2
Configure monitoring and logging for Azure ML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-online-endpoints?view=azureml-api-2
Extend Azure ML prebuilt inference images with Pythonhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-prebuilt-docker-images-inference-python-extensibility?view=azureml-api-1
Use R and RStudio on Azure Machine Learning computehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-r-interactive-development?view=azureml-api-2
Use Responsible AI dashboard tools in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard?view=azureml-api-2
Generate Responsible AI insights in Azure ML studiohttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-insights-ui?view=azureml-api-2
Configure and export Responsible AI scorecards in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-scorecard?view=azureml-api-2
Schedule recurring data imports in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-schedule-data-import?view=azureml-api-2
Configure Azure ML training compute targets (SDK v1)https://learn.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets?view=azureml-api-1
Share data assets across Azure ML workspaces via registrieshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-share-data-across-workspaces-with-registries?view=azureml-api-2
Share models and components across Azure ML workspaceshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-share-models-pipelines-across-workspaces-with-registries?view=azureml-api-2
Query and compare MLflow experiments and runs in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments-mlflow?view=azureml-api-2
Submit MLflow Projects training jobs to Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-mlflow-projects?view=azureml-api-2
Configure and submit Azure ML training jobs (v2)https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-model?view=azureml-api-2
Configure and submit Azure ML training jobs (v2)https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-model?view=azureml-api-2
Train Azure ML models using custom Docker images (v1)https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-with-custom-image?view=azureml-api-1
Configure hyperparameter sweep jobs in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2
Configure AutoMLStep in Azure ML pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automlstep-in-pipelines?view=azureml-api-1
Use MLflow to track Azure ML experiments and runshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-cli-runs?view=azureml-api-2
Configure MLflow tracking with Azure Machine Learning workspaceshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-configure-tracking?view=azureml-api-2
Configure and run parallel jobs in Azure ML pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-job-in-pipeline?view=azureml-api-2
Configure pipeline parameters in Azure ML designerhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-pipeline-parameter?view=azureml-api-1
Run training jobs on Azure ML serverless computehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2
Configure hyperparameter sweep in Azure ML pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-sweep-in-pipeline?view=azureml-api-2
Configure dataset versioning in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-version-track-datasets?view=azureml-api-1
View and tag costs for Azure ML managed online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-view-online-endpoints-costs?view=azureml-api-2
Configure serverless Spark compute for Azure ML notebookshttps://learn.microsoft.com/en-us/azure/machine-learning/interactive-data-wrangling-with-apache-spark-azure-ml?view=azureml-api-2
Reference Azure Machine Learning monitoring metrics and logshttps://learn.microsoft.com/en-us/azure/machine-learning/monitor-azure-machine-learning-reference?view=azureml-api-2
Configure custom base images for prompt flow sessionshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-customize-session-base-image?view=azureml-api-2
Configure and consume streaming responses from prompt flow endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-enable-streaming-mode?view=azureml-api-2
Enable tracing and user feedback collection for prompt flow deploymentshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-enable-trace-feedback-for-deployment?view=azureml-api-2
Configure and manage prompt flow compute sessions in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-manage-compute-session?view=azureml-api-2
Configure monitoring for Azure ML generative AI appshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-monitor-generative-ai-applications?view=azureml-api-2
Configure Azure OpenAI GPT-4 Turbo with Vision toolhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/azure-open-ai-gpt-4v-tool?view=azureml-api-2
Configure Content Safety text tool in prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/content-safety-text-tool?view=azureml-api-2
Configure embedding tool for OpenAI in prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/embedding-tool?view=azureml-api-2
Configure Index Lookup tool for RAG in prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/index-lookup-tool?view=azureml-api-2
Configure LLM tool in Azure ML prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/llm-tool?view=azureml-api-2
Use Open Model LLM tool in Azure ML prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/open-model-llm-tool?view=azureml-api-2
Configure OpenAI GPT-4V tool in Azure ML prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/openai-gpt-4v-tool?view=azureml-api-2
Configure and manage tools in Azure ML prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/overview?view=azureml-api-2
Use and configure prompt templates in prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/prompt-tool?view=azureml-api-2
Create and configure Python tools in prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/python-tool?view=azureml-api-2
Configure Rerank tool for RAG in prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/rerank-tool?view=azureml-api-2
Configure SerpAPI search tool in Azure ML prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/serp-api-tool?view=azureml-api-2
Configure Automated ML forecasting jobs via YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automated-ml-forecasting?view=azureml-api-2
Author AutoML image classification jobs in YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-classification?view=azureml-api-2
Define AutoML image instance segmentation YAML jobshttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-instance-segmentation?view=azureml-api-2
Configure AutoML image multilabel classification YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-multilabel-classification?view=azureml-api-2
Author AutoML image object detection YAML jobshttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-object-detection?view=azureml-api-2
Configure AutoML vision hyperparameters in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-hyperparameters?view=azureml-api-2
Format JSONL data for AutoML computer visionhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-schema?view=azureml-api-2
Configure AutoML multilabel text classification YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-multilabel-classification?view=azureml-api-2
Author AutoML NLP NER jobs using YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-ner?view=azureml-api-2
Define AutoML text classification jobs with YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-text-classification?view=azureml-api-2
Reference configuration for Kubernetes with Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-kubernetes?view=azureml-api-2
Define command components via Azure ML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-command?view=azureml-api-2
Author pipeline components using Azure ML YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-pipeline?view=azureml-api-2
Configure Spark components in Azure ML YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-spark?view=azureml-api-2
Configure AmlCompute clusters via YAML in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-aml?view=azureml-api-2
Define Azure ML compute instances with YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-instance?view=azureml-api-2
Configure attached Kubernetes clusters in Azure ML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-kubernetes?view=azureml-api-2
Attach and configure VMs via Azure ML YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-vm?view=azureml-api-2
Configure AI Content Safety connections in AML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-content-safety?view=azureml-api-2
Author AI Search connection YAML for AMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-search?view=azureml-api-2
Configure Foundry Tools connections with Azure ML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-services?view=azureml-api-2
Define API key connections via AML YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-api-key?view=azureml-api-2
Define Azure OpenAI connections via AML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-azure-openai?view=azureml-api-2
Define blob datastore connections in AML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-blob?view=azureml-api-2
Configure Azure Container Registry connections in AMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-container-registry?view=azureml-api-2
Author custom key connections in Azure ML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-custom-key?view=azureml-api-2
Configure Data Lake Gen2 connections via AML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-data-lake?view=azureml-api-2
Configure Git repository connections in AML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-git?view=azureml-api-2
Set up OneLake connections using AML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-onelake?view=azureml-api-2
Configure OpenAI service connections in AML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-openai?view=azureml-api-2
Set up Python feed connections using AML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-python-feed?view=azureml-api-2
Define Serp connections via Azure ML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-serp?view=azureml-api-2
Author serverless connection YAML for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-serverless?view=azureml-api-2
Configure AI Speech Services connections in AML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-speech?view=azureml-api-2
Understand core Azure ML CLI v2 YAML syntaxhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-core-syntax?view=azureml-api-2
Reference schema for Azure ML data YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-data?view=azureml-api-2
Define Azure Blob datastores via YAML in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-blob?view=azureml-api-2
Author Azure Data Lake Gen1 datastore YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-data-lake-gen1?view=azureml-api-2
Configure Azure Data Lake Gen2 datastores in YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-data-lake-gen2?view=azureml-api-2
Configure Azure Files datastores using YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-files?view=azureml-api-2
Author batch deployment YAML for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-batch?view=azureml-api-2
Define Kubernetes online deployments in Azure ML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-kubernetes-online?view=azureml-api-2
Configure managed online deployments via YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-managed-online?view=azureml-api-2
Author deployment template YAML for Azure ML CLI v2https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-template?view=azureml-api-2
Author batch endpoint YAML for Azure ML CLI v2https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-endpoint-batch?view=azureml-api-2
Configure Azure ML online endpoints with YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-endpoint-online?view=azureml-api-2
Reference schema for Azure ML environment YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-environment?view=azureml-api-2
Author feature entity definitions via Azure ML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-entity?view=azureml-api-2
Create feature retrieval specs with Azure ML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-retrieval-spec?view=azureml-api-2
Configure feature sets in Azure ML YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-set?view=azureml-api-2
Define feature stores in Azure ML using YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-store?view=azureml-api-2
Define feature set specifications using YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-featureset-spec?view=azureml-api-2
Author command job YAML for Azure ML CLI v2https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-command?view=azureml-api-2
Create parallel jobs in Azure ML pipeline YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-parallel?view=azureml-api-2
Author pipeline job definitions with AML YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-pipeline?view=azureml-api-2
Configure Azure ML pipeline jobs using YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-pipeline?view=azureml-api-2
Configure Spark jobs in Azure ML with YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-spark?view=azureml-api-2
Define sweep (hyperparameter) jobs with Azure ML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-sweep?view=azureml-api-2
Reference schema for Azure ML MLTable YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable?view=azureml-api-2
Define Azure ML models using CLI v2 YAML schemahttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-model?view=azureml-api-2
Create model monitoring schedules with Azure ML YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-monitor?view=azureml-api-2
Navigate Azure ML CLI v2 YAML schema referenceshttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-overview?view=azureml-api-2
Define Azure ML registries using CLI v2 YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-registry?view=azureml-api-2
Author data import schedule YAML for Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-schedule-data-import?view=azureml-api-2
Configure Azure ML job schedules with YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-schedule?view=azureml-api-2
Reference schema for Azure ML workspace YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-workspace?view=azureml-api-2
主题URL
使用ML表配置AutoML分类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/classification?view=azureml-api-2
在设计器中配置AutoML预测组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/forecasting?view=azureml-api-2
配置AutoML图像多标签分类https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-classification-multilabel?view=azureml-api-2
配置AutoML图像分类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-classification?view=azureml-api-2
配置AutoML图像实例分割组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-instance-segmentation?view=azureml-api-2
配置AutoML图像目标检测组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-object-detection?view=azureml-api-2
使用ML表配置AutoML回归组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/regression?view=azureml-api-2
配置AutoML文本多标签分类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-classification-multilabel?view=azureml-api-2
配置AutoML文本分类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-classification?view=azureml-api-2
在Azure ML中配置AutoML文本NER组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-ner?view=azureml-api-2
配置添加列组件以连接数据集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/add-columns?view=azureml-api-2
配置添加行组件以追加数据集记录https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/add-rows?view=azureml-api-2
在Azure ML中配置应用图像转换https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-image-transformation?view=azureml-api-2
配置应用数学运算组件以进行列计算https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-math-operation?view=azureml-api-2
使用SQLite配置应用SQL转换组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-sql-transformation?view=azureml-api-2
在Azure ML中配置应用转换组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-transformation?view=azureml-api-2
在Azure ML中配置将数据分配到集群https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/assign-data-to-clusters?view=azureml-api-2
配置提升决策树回归组件（LightGBM）https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/boosted-decision-tree-regression?view=azureml-api-2
配置清理缺失数据组件以处理空值https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/clean-missing-data?view=azureml-api-2
配置裁剪值组件以处理异常值https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/clip-values?view=azureml-api-2
配置并使用Azure ML设计器算法组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/component-reference?view=azureml-api-2
配置转换为CSV组件以导出数据集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-csv?view=azureml-api-2
配置转换为数据集组件以使用内部格式https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-dataset?view=azureml-api-2
在Azure ML中配置转换为图像目录https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-image-directory?view=azureml-api-2
配置转换为指示值以进行分类编码https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-indicator-values?view=azureml-api-2
在Azure ML中配置转换词为向量组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-word-to-vector?view=azureml-api-2
在Azure ML中配置创建Python模型组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/create-python-model?view=azureml-api-2
在Azure ML中配置交叉验证模型组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/cross-validate-model?view=azureml-api-2
在Azure ML设计器中配置决策森林回归https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/decision-forest-regression?view=azureml-api-2
配置DenseNet图像分类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/densenet?view=azureml-api-2
配置编辑元数据组件以调整列角色https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/edit-metadata?view=azureml-api-2
设置手动输入数据组件以处理小数据集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/enter-data-manually?view=azureml-api-2
在Azure ML中配置评估模型组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-model?view=azureml-api-2
配置评估推荐器组件以评估模型准确性https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-recommender?view=azureml-api-2
在Azure ML设计器中配置执行Python脚本https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/execute-python-script?view=azureml-api-2
在Azure ML中配置执行R脚本组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/execute-r-script?view=azureml-api-2
配置导出数据组件以保存管道输出https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/export-data?view=azureml-api-2
在Azure ML中配置从文本提取N-Gram特征https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/extract-n-gram-features-from-text?view=azureml-api-2
在Azure ML中配置快速森林分位数回归https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/fast-forest-quantile-regression?view=azureml-api-2
在Azure ML中配置特征哈希文本组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/feature-hashing?view=azureml-api-2
配置基于过滤的特征选择以选择预测列https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/filter-based-feature-selection?view=azureml-api-2
在Azure ML设计器中使用图形搜索查询语法https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/graph-search-syntax?view=azureml-api-2
配置将数据分组到箱中组件以进行离散化https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/group-data-into-bins?view=azureml-api-2
为Azure ML设计器管道配置导入数据组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/import-data?view=azureml-api-2
在Azure ML设计器中配置初始化图像转换https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/init-image-transformation?view=azureml-api-2
配置连接数据组件以合并数据集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/join-data?view=azureml-api-2
在Azure ML中配置K-Means聚类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/k-means-clustering?view=azureml-api-2
在Azure ML中配置潜在狄利克雷分配组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/latent-dirichlet-allocation?view=azureml-api-2
在Azure ML中配置线性回归组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/linear-regression?view=azureml-api-2
在Azure ML中配置多类提升决策树https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-boosted-decision-tree?view=azureml-api-2
在Azure ML中配置多类决策森林https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-decision-forest?view=azureml-api-2
在Azure ML中配置多类逻辑回归https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-logistic-regression?view=azureml-api-2
在Azure ML中配置多类神经网络https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-neural-network?view=azureml-api-2
在Azure ML中设置神经网络回归https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/neural-network-regression?view=azureml-api-2
配置标准化数据组件以进行特征缩放https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/normalize-data?view=azureml-api-2
在Azure ML中配置一对多多类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/one-vs-all-multiclass?view=azureml-api-2
在Azure ML中配置一对一多类组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/one-vs-one-multiclass?view=azureml-api-2
配置分区和采样组件以拆分数据集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/partition-and-sample?view=azureml-api-2
配置已弃用的基于PCA的异常检测组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/pca-based-anomaly-detection?view=azureml-api-2
配置排列特征重要性组件以获取模型洞察https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/permutation-feature-importance?view=azureml-api-2
在Azure ML设计器中使用泊松回归组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/poisson-regression?view=azureml-api-2
在Azure ML中配置预处理文本组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/preprocess-text?view=azureml-api-2
配置删除重复行组件以进行去重https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/remove-duplicate-rows?view=azureml-api-2
在Azure ML中配置ResNet图像分类https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/resnet?view=azureml-api-2
在Azure ML中配置评分图像模型组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-image-model?view=azureml-api-2
在Azure ML设计器中配置评分模型组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-model?view=azureml-api-2
配置评分SVD推荐器以进行预测https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-svd-recommender?view=azureml-api-2
在Azure ML中配置评分Vowpal Wabbit模型https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-vowpal-wabbit-model?view=azureml-api-2
配置评分深度与广度推荐器组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-wide-and-deep-recommender?view=azureml-api-2
配置在数据集中选择列以选择特征子集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/select-columns-in-dataset?view=azureml-api-2
配置选择列转换以使用稳定特征集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/select-columns-transform?view=azureml-api-2
配置SMOTE组件以对少数类进行过采样https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/smote?view=azureml-api-2
配置拆分数据组件以进行训练-测试分区https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/split-data?view=azureml-api-2
配置拆分图像目录组件以处理数据集https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/split-image-directory?view=azureml-api-2
配置汇总数据组件以获取描述性统计https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/summarize-data?view=azureml-api-2
配置训练异常检测模型组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-anomaly-detection-model?view=azureml-api-2
在Azure ML中配置训练聚类模型组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-clustering-model?view=azureml-api-2
在Azure ML中配置训练PyTorch模型组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-pytorch-model?view=azureml-api-2
在Azure ML设计器中配置训练SVD推荐器https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-svd-recommender?view=azureml-api-2
在Azure ML中配置训练Vowpal Wabbit模型https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-vowpal-wabbit-model?view=azureml-api-2
配置训练深度与广度推荐器组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-wide-and-deep-recommender?view=azureml-api-2
在Azure ML中配置调优模型超参数https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/tune-model-hyperparameters?view=azureml-api-2
在Azure ML中配置两类平均感知器https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?view=azureml-api-2
在Azure ML中配置两类提升决策树https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-boosted-decision-tree?view=azureml-api-2
在Azure ML中配置两类决策森林https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-decision-forest?view=azureml-api-2
在Azure ML中配置两类逻辑回归https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-logistic-regression?view=azureml-api-2
在Azure ML中配置两类神经网络https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-neural-network?view=azureml-api-2
在Azure ML中配置两类SVM组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-support-vector-machine?view=azureml-api-2
配置Web服务输入和输出组件https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/web-service-input-output?view=azureml-api-2
在Azure ML SDK和CLI v2作业中使用表达式https://learn.microsoft.com/en-us/azure/machine-learning/concept-expressions?view=azureml-api-2
为Azure ML在线部署指定模型https://learn.microsoft.com/en-us/azure/machine-learning/concept-online-deployment-model-specification?view=azureml-api-2
使用Azure ML预构建Docker镜像进行推理https://learn.microsoft.com/en-us/azure/machine-learning/concept-prebuilt-docker-images-inference?view=azureml-api-2
配置并使用Azure ML负责任AI仪表板https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard?view=azureml-api-2
在Azure ML中使用工作区软删除和恢复https://learn.microsoft.com/en-us/azure/machine-learning/concept-soft-delete?view=azureml-api-2
为Azure ML训练作业配置Git集成https://learn.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration?view=azureml-api-2
配置并使用Azure ML中的向量存储https://learn.microsoft.com/en-us/azure/machine-learning/concept-vector-stores?view=azureml-api-2
通过数据存储UI将OneLake表链接到Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/create-datastore-with-user-interface?view=azureml-api-2
为训练和推理配置特征检索规范https://learn.microsoft.com/en-us/azure/machine-learning/feature-retrieval-concepts?view=azureml-api-2
在Azure ML中配置特征集实例化https://learn.microsoft.com/en-us/azure/machine-learning/feature-set-materialization-concepts?view=azureml-api-2
交互式ML开发期间访问Azure云存储数据https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-data-interactive?view=azureml-api-2
为Azure ML配置Kubernetes计算目标https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2
为Azure ML配置Kubernetes计算目标https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2
为Azure ML配置Kubernetes计算目标https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2
将Kubernetes集群附加到Azure ML工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-to-workspace?view=azureml-api-2
为时间序列预测配置Azure AutoMLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-forecast?view=azureml-api-2
在Azure ML中配置AutoML计算机视觉训练https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?view=azureml-api-2
为自定义NLP训练配置Azure AutoMLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-nlp-models?view=azureml-api-2
为Azure ML在线端点配置自动缩放https://learn.microsoft.com/en-us/azure/machine-learning/how-to-autoscale-endpoints?view=azureml-api-2
为PyTorch环境配置自定义Azure容器https://learn.microsoft.com/en-us/azure/machine-learning/how-to-azure-container-for-pytorch-environment?view=azureml-api-2
为Azure ML端点启用生产推理数据收集https://learn.microsoft.com/en-us/azure/machine-learning/how-to-collect-production-data?view=azureml-api-2
在Azure ML中自定义AutoML数据特征化设置https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-features?view=azureml-api-1
使用SDK v2配置Azure AutoML表格训练https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train?view=azureml-api-2
在Azure AutoML中配置数据拆分和交叉验证https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits?view=azureml-api-1
配置Azure ML与外部数据和服务的连接https://learn.microsoft.com/en-us/azure/machine-learning/how-to-connection?view=azureml-api-2
创建和管理Azure ML计算集群https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?view=azureml-api-2
在工作室中配置和管理Azure ML计算https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio?view=azureml-api-2
创建Azure ML计算实例用于开发https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-compute-instance?view=azureml-api-2
创建Azure ML计算实例用于开发https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-compute-instance?view=azureml-api-2
创建和管理Azure ML数据资产https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets?view=azureml-api-2
创建和管理Azure ML数据资产https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets?view=azureml-api-2
在Azure ML中配置图像标注项目https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-image-labeling-projects?view=azureml-api-2
在Azure ML中配置文本标注项目https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-text-labeling-projects?view=azureml-api-2
在Azure ML prompt flow中创建和配置向量索引https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-vector-index?view=azureml-api-2
使用ARM模板创建Azure ML工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template?view=azureml-api-2
为Azure ML专用端点配置自定义DNShttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-custom-dns?view=azureml-api-2
使用启动脚本自定义Azure ML计算实例https://learn.microsoft.com/en-us/azure/machine-learning/how-to-customize-compute-instance?view=azureml-api-2
配置并使用Azure ML数据存储以访问存储https://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2
在Kubernetes集群上部署Azure ML扩展https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-kubernetes-extension?view=azureml-api-2
导出或删除Azure ML工作区数据https://learn.microsoft.com/en-us/azure/machine-learning/how-to-export-delete-data?view=azureml-api-2
自定义Azure ML预构建Docker镜像用于推理https://learn.microsoft.com/en-us/azure/machine-learning/how-to-extend-prebuilt-docker-image-inference?view=azureml-api-1
将外部数据导入Azure ML作为数据资产https://learn.microsoft.com/en-us/azure/machine-learning/how-to-import-data-assets?view=azureml-api-2
在Azure ML项目中标注图像和文本https://learn.microsoft.com/en-us/azure/machine-learning/how-to-label-data?view=azureml-api-2
使用Spark池链接Synapse和Azure ML工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-link-synapse-ml-workspaces?view=azureml-api-1
将MLflow模型作为一级模型记录到Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-mlflow-models?view=azureml-api-2
将Azure ML分布式训练日志发送到Application Insightshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-search?view=azureml-api-2
在Azure ML中配置模型可解释性https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability?view=azureml-api-2
管理Azure ML计算实例和生命周期https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-compute-instance?view=azureml-api-2
通过CLI和SDK配置Azure ML环境https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2
通过CLI和SDK管理Azure ML环境https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2
使用Bicep模板创建Azure ML中心工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-hub-workspace-template?view=azureml-api-2
管理导入数据资产的生命周期和自动删除https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-imported-data-assets?view=azureml-api-2
管理Azure ML中的组件和管道输入/输出https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-inputs-outputs-pipeline?view=azureml-api-2
创建和管理Azure ML Kubernetes实例类型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-kubernetes-instance-types?view=azureml-api-2
管理和导出Azure ML标注项目https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-labeling-projects?view=azureml-api-2
使用MLflow管理Azure ML模型注册表https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models-mlflow?view=azureml-api-2
使用Azure ML CLI和SDK注册和管理模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models?view=azureml-api-2
创建和管理Azure ML注册表https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-registries?view=azureml-api-2
使用VS Code扩展管理Azure ML资源https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-resources-vscode?view=azureml-api-2
附加和管理Azure ML中的Synapse Spark池https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-synapse-spark-pool?view=azureml-api-2
使用Terraform配置Azure ML工作区https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-terraform?view=azureml-api-2
在Azure ML中配置数据漂移监视器https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets?view=azureml-api-1
收集和监控Kubernetes端点推理日志https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-kubernetes-online-enpoint-inference-server-log?view=azureml-api-2
在生产环境中配置Azure ML模型性能监控https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-model-performance?view=azureml-api-2
为Azure ML在线端点配置监控和日志https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-online-endpoints?view=azureml-api-2
为Azure ML在线端点配置监控和日志https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-online-endpoints?view=azureml-api-2
使用Python扩展Azure ML预构建推理镜像https://learn.microsoft.com/en-us/azure/machine-learning/how-to-prebuilt-docker-images-inference-python-extensibility?view=azureml-api-1
在Azure Machine Learning计算上使用R和RStudiohttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-r-interactive-development?view=azureml-api-2
在Azure ML中使用负责任AI仪表板工具https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard?view=azureml-api-2
在Azure ML工作室中生成负责任AI洞察https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-insights-ui?view=azureml-api-2
在Azure ML中配置和导出负责任AI记分卡https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-scorecard?view=azureml-api-2
在Azure ML中计划定期数据导入https://learn.microsoft.com/en-us/azure/machine-learning/how-to-schedule-data-import?view=azureml-api-2
配置Azure ML训练计算目标（SDK v1）https://learn.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets?view=azureml-api-1
通过注册表在Azure ML工作区之间共享数据资产https://learn.microsoft.com/en-us/azure/machine-learning/how-to-share-data-across-workspaces-with-registries?view=azureml-api-2
在Azure ML工作区之间共享模型和组件https://learn.microsoft.com/en-us/azure/machine-learning/how-to-share-models-pipelines-across-workspaces-with-registries?view=azureml-api-2
在Azure ML中查询和比较MLflow实验和运行https://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments-mlflow?view=azureml-api-2
将MLflow Projects训练作业提交到Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-mlflow-projects?view=azureml-api-2
配置并提交Azure ML训练作业（v2）https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-model?view=azureml-api-2
配置并提交Azure ML训练作业（v2）https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-model?view=azureml-api-2
使用自定义Docker镜像训练Azure ML模型（v1）https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-with-custom-image?view=azureml-api-1
在Azure ML中配置超参数调优作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2
在Azure ML管道中配置AutoMLStephttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automlstep-in-pipelines?view=azureml-api-1
使用MLflow跟踪Azure ML实验和运行https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-cli-runs?view=azureml-api-2
配置MLflow与Azure Machine Learning工作区的跟踪https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-configure-tracking?view=azureml-api-2
在Azure ML管道中配置并运行并行作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-job-in-pipeline?view=azureml-api-2
在Azure ML设计器中配置管道参数https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-pipeline-parameter?view=azureml-api-1
在Azure ML无服务器计算上运行训练作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2
在Azure ML管道中配置超参数调优https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-sweep-in-pipeline?view=azureml-api-2
在Azure ML中配置数据集版本控制https://learn.microsoft.com/en-us/azure/machine-learning/how-to-version-track-datasets?view=azureml-api-1
查看和标记Azure ML托管在线端点的成本https://learn.microsoft.com/en-us/azure/machine-learning/how-to-view-online-endpoints-costs?view=azureml-api-2
为Azure ML笔记本配置无服务器Spark计算https://learn.microsoft.com/en-us/azure/machine-learning/interactive-data-wrangling-with-apache-spark-azure-ml?view=azureml-api-2
参考Azure Machine Learning监控指标和日志https://learn.microsoft.com/en-us/azure/machine-learning/monitor-azure-machine-learning-reference?view=azureml-api-2
为prompt flow会话配置自定义基础镜像https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-customize-session-base-image?view=azureml-api-2
配置并使用prompt flow端点的流式响应https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-enable-streaming-mode?view=azureml-api-2
为prompt flow部署启用跟踪和用户反馈收集https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-enable-trace-feedback-for-deployment?view=azureml-api-2
在Azure ML中配置和管理prompt flow计算会话https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-manage-compute-session?view=azureml-api-2
为Azure ML生成式AI应用配置监控https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-monitor-generative-ai-applications?view=azureml-api-2
配置Azure OpenAI GPT-4 Turbo with Vision工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/azure-open-ai-gpt-4v-tool?view=azureml-api-2
在prompt flow中配置内容安全文本工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/content-safety-text-tool?view=azureml-api-2
在prompt flow中配置OpenAI嵌入工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/embedding-tool?view=azureml-api-2
在prompt flow中配置RAG用索引查找工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/index-lookup-tool?view=azureml-api-2
在Azure ML prompt flow中配置LLM工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/llm-tool?view=azureml-api-2
在Azure ML prompt flow中使用Open Model LLM工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/open-model-llm-tool?view=azureml-api-2
在Azure ML prompt flow中配置OpenAI GPT-4V工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/openai-gpt-4v-tool?view=azureml-api-2
在Azure ML prompt flow中配置和管理工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/overview?view=azureml-api-2
在prompt flow中使用和配置提示词模板https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/prompt-tool?view=azureml-api-2
在prompt flow中创建和配置Python工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/python-tool?view=azureml-api-2
在prompt flow中配置RAG用重排序工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/rerank-tool?view=azureml-api-2
在Azure ML prompt flow中配置SerpAPI搜索工具https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/serp-api-tool?view=azureml-api-2
通过YAML配置自动化ML预测作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automated-ml-forecasting?view=azureml-api-2
使用YAML编写AutoML图像分类作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-classification?view=azureml-api-2
定义AutoML图像实例分割YAML作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-instance-segmentation?view=azureml-api-2
配置AutoML图像多标签分类YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-multilabel-classification?view=azureml-api-2
使用YAML编写AutoML图像目标检测作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-object-detection?view=azureml-api-2
在Azure ML中配置AutoML视觉超参数https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-hyperparameters?view=azureml-api-2
为AutoML计算机视觉格式化JSONL数据https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-schema?view=azureml-api-2
配置AutoML多标签文本分类YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-multilabel-classification?view=azureml-api-2
使用YAML架构编写AutoML NLP NER作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-ner?view=azureml-api-2
使用YAML定义AutoML文本分类作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-text-classification?view=azureml-api-2
参考Azure ML与Kubernetes的配置https://learn.microsoft.com/en-us/azure/machine-learning/reference-kubernetes?view=azureml-api-2
通过Azure ML YAML定义命令组件https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-command?view=azureml-api-2
使用Azure ML YAML架构编写管道组件https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-pipeline?view=azureml-api-2
在Azure ML YAML架构中配置Spark组件https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-spark?view=azureml-api-2
通过YAML在Azure ML中配置AmlCompute集群https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-aml?view=azureml-api-2
使用YAML定义Azure ML计算实例https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-instance?view=azureml-api-2
在Azure ML YAML中配置附加的Kubernetes集群https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-kubernetes?view=azureml-api-2
通过Azure ML YAML架构附加和配置VMhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-vm?view=azureml-api-2
在AML YAML中配置AI内容安全连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-content-safety?view=azureml-api-2
为AML编写AI搜索连接YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-search?view=azureml-api-2
使用Azure ML YAML配置Foundry Tools连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-services?view=azureml-api-2
通过AML YAML架构定义API密钥连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-api-key?view=azureml-api-2
通过AML YAML定义Azure OpenAI连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-azure-openai?view=azureml-api-2
在AML YAML中定义Blob数据存储连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-blob?view=azureml-api-2
在AML中配置Azure容器注册表连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-container-registry?view=azureml-api-2
在Azure ML YAML中编写自定义密钥连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-custom-key?view=azureml-api-2
通过AML YAML配置Data Lake Gen2连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-data-lake?view=azureml-api-2
在AML YAML中配置Git仓库连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-git?view=azureml-api-2
使用AML YAML设置OneLake连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-onelake?view=azureml-api-2
在AML YAML中配置OpenAI服务连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-openai?view=azureml-api-2
使用AML YAML设置Python源连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-python-feed?view=azureml-api-2
通过Azure ML YAML定义Serp连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-serp?view=azureml-api-2
为Azure ML编写无服务器连接YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-serverless?view=azureml-api-2
在AML YAML中配置AI语音服务连接https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-speech?view=azureml-api-2
了解核心Azure ML CLI v2 YAML语法https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-core-syntax?view=azureml-api-2
参考Azure ML数据YAML的架构https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-data?view=azureml-api-2
通过YAML在Azure ML中定义Azure Blob数据存储https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-blob?view=azureml-api-2
编写Azure Data Lake Gen1数据存储YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-data-lake-gen1?view=azureml-api-2
在YAML中配置Azure Data Lake Gen2数据存储https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-data-lake-gen2?view=azureml-api-2
使用YAML架构配置Azure Files数据存储https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-files?view=azureml-api-2
为Azure ML编写批量部署YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-batch?view=azureml-api-2
在Azure ML YAML中定义Kubernetes在线部署https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-kubernetes-online?view=azureml-api-2
通过YAML配置托管在线部署https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-managed-online?view=azureml-api-2
为Azure ML CLI v2编写部署模板YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-template?view=azureml-api-2
为Azure ML CLI v2编写批量端点YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-endpoint-batch?view=azureml-api-2
使用YAML配置Azure ML在线端点https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-endpoint-online?view=azureml-api-2
参考Azure ML环境YAML的架构https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-environment?view=azureml-api-2
通过Azure ML YAML编写特征实体定义https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-entity?view=azureml-api-2
使用Azure ML YAML创建特征检索规范https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-retrieval-spec?view=azureml-api-2
在Azure ML YAML架构中配置特征集https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-set?view=azureml-api-2
使用YAML在Azure ML中定义特征存储https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-store?view=azureml-api-2
使用YAML架构定义特征集规范https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-featureset-spec?view=azureml-api-2
为Azure ML CLI v2编写命令作业YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-command?view=azureml-api-2
在Azure ML管道YAML中创建并行作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-parallel?view=azureml-api-2
使用AML YAML架构编写管道作业定义https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-pipeline?view=azureml-api-2
使用YAML架构配置Azure ML管道作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-pipeline?view=azureml-api-2
使用YAML在Azure ML中配置Spark作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-spark?view=azureml-api-2
使用Azure ML YAML定义调优（超参数）作业https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-sweep?view=azureml-api-2
参考Azure ML MLTable YAML的架构https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable?view=azureml-api-2
使用CLI v2 YAML架构定义Azure ML模型https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-model?view=azureml-api-2
使用Azure ML YAML创建模型监控计划https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-monitor?view=azureml-api-2
浏览Azure ML CLI v2 YAML架构参考https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-overview?view=azureml-api-2
使用CLI v2 YAML定义Azure ML注册表https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-registry?view=azureml-api-2
为Azure ML编写数据导入计划YAMLhttps://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-schedule-data-import?view=azureml-api-2
使用YAML配置Azure ML作业计划https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-schedule?view=azureml-api-2
参考Azure ML工作区YAML的架构https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-workspace?view=azureml-api-2

Integrations & Coding Patterns

集成与编码模式

TopicURL
Configure input data sources for AML batch endpoint jobshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-data-batch-endpoints-jobs?view=azureml-api-2
Set up Azure Databricks with AutoML in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-databricks-automl-environment?view=azureml-api-1
Connect storage to Azure ML via studio UIhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-connect-data-ui?view=azureml-api-1
Ingest data to Azure ML with Data Factoryhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-data-ingest-adf?view=azureml-api-1
Wrangle data using Synapse Spark with Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-data-prep-synapse-spark-pool?view=azureml-api-1
Configure Azure ML datastores for storage accesshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2
Configure Azure ML datastores for storage accesshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2
Deploy AML models as custom skills for Azure AI Searchhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-model-cognitive-search?view=azureml-api-1
Deploy Hugging Face transformer models to Azure ML endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-from-huggingface?view=azureml-api-2
Use Azure ML REST API for online deploymentshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-rest?view=azureml-api-2
Import data into Azure ML designerhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-designer-import-data?view=azureml-api-1
Run custom Python code in Azure ML designer pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-designer-python?view=azureml-api-1
Run local ONNX inference for Azure AutoML image modelshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-inference-onnx-automl-image-models?view=azureml-api-2
Use Azure ML inference HTTP server for local debugginghttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-inference-server-http?view=azureml-api-2
Log metrics and artifacts with MLflow in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics?view=azureml-api-2
Manage Azure ML resources using REST APIshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-rest?view=azureml-api-2
Define and use MLTable data in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2
Securely integrate Azure Synapse with Azure ML via VNetshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-private-endpoint-integration-synapse?view=azureml-api-2
Read and write data in Azure ML jobshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2?view=azureml-api-2
Read and write data in Azure ML jobshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2?view=azureml-api-2
Generate Responsible AI dashboards with Azure ML SDKhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-insights-sdk-cli?view=azureml-api-2
Attach secured Azure Databricks to Azure ML via private endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-securely-attach-databricks?view=azureml-api-2
Submit standalone and pipeline Spark jobs in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-submit-spark-jobs?view=azureml-api-2
Log metrics in Azure ML designer pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-designer-experiments?view=azureml-api-1
Train PyTorch models using Azure ML SDK v2https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch?view=azureml-api-2
Use Azure AutoML ONNX models with ML.NET in .NET appshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automl-onnx-model-dotnet?view=azureml-api-2
Invoke Azure ML batch endpoints from Azure Data Factoryhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-azure-data-factory?view=azureml-api-2
Access Azure ML batch endpoints from Microsoft Fabrichttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-fabric?view=azureml-api-2
Trigger AML batch endpoints from Event Grid and storage eventshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid-batch?view=azureml-api-2
Integrate Azure ML events with Azure Event Gridhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid?view=azureml-api-2
Use labeled datasets from Azure ML labelinghttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-labeled-dataset?view=azureml-api-1
Integrate Azure Databricks MLflow tracking with Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks?view=azureml-api-2
Configure MLflow tracking from Azure Synapse to Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-synapse?view=azureml-api-2
Integrate Azure Synapse Spark in Azure ML pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-synapsesparkstep?view=azureml-api-1
Create and use custom tool packages in Azure ML prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-custom-tool-package-creation-and-usage?view=azureml-api-2
Evaluate Semantic Kernel plugins and planners with prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-evaluate-semantic-kernel?view=azureml-api-2
Integrate LangChain workflows with Azure ML prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-integrate-with-langchain?view=azureml-api-2
Incorporate image inputs into Azure ML prompt flowshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-process-image?view=azureml-api-2
Quickstart: Configure Spark jobs in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/quickstart-spark-jobs?view=azureml-api-2
Map Azure ML v1 logging APIs to MLflow trackinghttps://learn.microsoft.com/en-us/azure/machine-learning/reference-migrate-sdk-v1-mlflow-tracking?view=azureml-api-2
主题URL
为AML批量端点作业配置输入数据源https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-data-batch-endpoints-jobs?view=azureml-api-2
在Azure ML中设置Azure Databricks与AutoMLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-databricks-automl-environment?view=azureml-api-1
通过工作室UI将存储连接到Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-connect-data-ui?view=azureml-api-1
使用Data Factory将数据导入Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-data-ingest-adf?view=azureml-api-1
使用Synapse Spark与Azure ML进行数据处理https://learn.microsoft.com/en-us/azure/machine-learning/how-to-data-prep-synapse-spark-pool?view=azureml-api-1
配置Azure ML数据存储以访问存储https://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2
配置Azure ML数据存储以访问存储https://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2
将AML模型部署为Azure AI Search的自定义技能https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-model-cognitive-search?view=azureml-api-1
将Hugging Face Transformer模型部署到Azure ML端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-from-huggingface?view=azureml-api-2
使用Azure ML REST API进行在线部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-rest?view=azureml-api-2
将数据导入Azure ML设计器https://learn.microsoft.com/en-us/azure/machine-learning/how-to-designer-import-data?view=azureml-api-1
在Azure ML设计器管道中运行自定义Python代码https://learn.microsoft.com/en-us/azure/machine-learning/how-to-designer-python?view=azureml-api-1
为Azure AutoML图像模型运行本地ONNX推理https://learn.microsoft.com/en-us/azure/machine-learning/how-to-inference-onnx-automl-image-models?view=azureml-api-2
使用Azure ML推理HTTP服务器进行本地调试https://learn.microsoft.com/en-us/azure/machine-learning/how-to-inference-server-http?view=azureml-api-2
在Azure ML中使用MLflow记录指标和工件https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics?view=azureml-api-2
使用REST API管理Azure ML资源https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-rest?view=azureml-api-2
在Azure ML中定义和使用MLTable数据https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2
通过VNet将Azure Synapse与Azure ML安全集成https://learn.microsoft.com/en-us/azure/machine-learning/how-to-private-endpoint-integration-synapse?view=azureml-api-2
在Azure ML作业中读写数据https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2?view=azureml-api-2
在Azure ML作业中读写数据https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2?view=azureml-api-2
使用Azure ML SDK生成负责任AI仪表板https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-insights-sdk-cli?view=azureml-api-2
通过专用端点将受保护的Azure Databricks附加到Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-securely-attach-databricks?view=azureml-api-2
在Azure ML中提交独立和管道Spark作业https://learn.microsoft.com/en-us/azure/machine-learning/how-to-submit-spark-jobs?view=azureml-api-2
在Azure ML设计器管道中记录指标https://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-designer-experiments?view=azureml-api-1
使用Azure ML SDK v2训练PyTorch模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch?view=azureml-api-2
在.NET应用中使用Azure AutoML ONNX模型与ML.NEThttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automl-onnx-model-dotnet?view=azureml-api-2
从Azure Data Factory调用Azure ML批量端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-azure-data-factory?view=azureml-api-2
从Microsoft Fabric访问Azure ML批量端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-fabric?view=azureml-api-2
从Event Grid和存储事件触发AML批量端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid-batch?view=azureml-api-2
将Azure ML事件与Azure Event Grid集成https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid?view=azureml-api-2
使用Azure ML标注中的已标注数据集https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-labeled-dataset?view=azureml-api-1
将Azure Databricks MLflow跟踪与Azure ML集成https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks?view=azureml-api-2
配置MLflow从Azure Synapse到Azure ML的跟踪https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-synapse?view=azureml-api-2
在Azure ML管道中集成Azure Synapse Sparkhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-synapsesparkstep?view=azureml-api-1
在Azure ML prompt flow中创建和使用自定义工具包https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-custom-tool-package-creation-and-usage?view=azureml-api-2
使用prompt flow评估Semantic Kernel插件和规划器https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-evaluate-semantic-kernel?view=azureml-api-2
将LangChain工作流与Azure ML prompt flow集成https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-integrate-with-langchain?view=azureml-api-2
在Azure ML prompt flow中加入图像输入https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-process-image?view=azureml-api-2
快速入门：在Azure ML中配置Spark作业https://learn.microsoft.com/en-us/azure/machine-learning/quickstart-spark-jobs?view=azureml-api-2
将Azure ML v1日志API映射到MLflow跟踪https://learn.microsoft.com/en-us/azure/machine-learning/reference-migrate-sdk-v1-mlflow-tracking?view=azureml-api-2

Deployment

部署

TopicURL
Consume Azure ML standard deployments across workspaceshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-connect-models-serverless?view=azureml-api-2
Convert ML notebooks to production scripts with MLOpsPythonhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-convert-ml-experiment-to-production?view=azureml-api-1
Deploy AutoML models to AML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-automl-endpoint?view=azureml-api-2
Deploy AML models to Azure Container Instances with CLI v1https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-container-instance?view=azureml-api-1
Deploy AML models to Azure Kubernetes Service with SDK/CLI v1https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service?view=azureml-api-1
Deploy custom-container models to AML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-custom-container?view=azureml-api-2
Run MLflow models in Azure ML Spark jobshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-model-spark-jobs?view=azureml-api-2
Progressively roll out MLflow models on Azure ML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-progressive?view=azureml-api-2
Deploy MLflow models to Azure ML endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models?view=azureml-api-2
Customize batch deployment outputs in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-model-custom-output?view=azureml-api-2
Deploy catalog models as standard deployments in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-serverless?view=azureml-api-2
Deploy machine learning models to Azure ML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to AML managed online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to AML managed online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to AML managed online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to AML managed online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to AML managed online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy Azure ML pipelines as batch endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-pipeline-component-as-batch-endpoint?view=azureml-api-2
Publish and run Azure ML pipelines in productionhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-pipelines?view=azureml-api-1
Serve models with NVIDIA Triton on AML endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-triton?view=azureml-api-2
Build Azure ML CI/CD pipelines with Azure DevOpshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-devops-machine-learning?view=azureml-api-2
Create GitHub Actions workflows for Azure ML CI/CDhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning?view=azureml-api-2
Deploy image-processing models with AML batch endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-image-processing-batch?view=azureml-api-2
Deploy MLflow models for batch inference with Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-mlflow-batch?view=azureml-api-2
Run language models with AML batch endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-nlp-processing-batch?view=azureml-api-2
Retrain Azure ML designer models via published pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-retrain-designer?view=azureml-api-1
Run Azure ML RAG prompt flows locally with VS Codehttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-retrieval-augmented-generation-cloud-to-local?view=azureml-api-2
Deploy and trigger batch prediction pipelines in Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer?view=azureml-api-1
Perform safe blue-green rollouts for Azure ML online endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints?view=azureml-api-2
Set up end-to-end MLOps with Azure DevOps and Azure MLhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-mlops-azureml?view=azureml-api-2
Trigger published Azure ML pipelines automaticallyhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-trigger-published-pipeline?view=azureml-api-1
Deploy models for batch scoring with AML batch endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-model-deployments?view=azureml-api-2
Run Azure OpenAI embeddings via AML batch endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-model-openai-embeddings?view=azureml-api-2
Deploy and invoke pipelines via AML batch endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-pipeline-deployments?view=azureml-api-2
Convert existing AML pipeline jobs to batch endpoint deploymentshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-pipeline-from-job?view=azureml-api-2
Operationalize scoring pipelines on AML batch endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-scoring-pipeline?view=azureml-api-2
Operationalize training pipelines on AML batch endpointshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-training-pipeline?view=azureml-api-2
Build RAG pipelines with Azure ML and prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-pipelines-prompt-flow?view=azureml-api-2
Deploy prompt flows as managed online endpoints for real-time inferencehttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-deploy-for-real-time-inference?view=azureml-api-2
Deploy prompt flows to managed or Kubernetes online endpoints with CLIhttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-deploy-to-code?view=azureml-api-2
Implement GenAIOps with prompt flow and Azure DevOps pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-end-to-end-azure-devops-with-prompt-flow?view=azureml-api-2
Implement GenAIOps with prompt flow and GitHub pipelineshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-end-to-end-llmops-with-prompt-flow?view=azureml-api-2
Integrate prompt flow with DevOps pipelines for LLM appshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-integrate-with-llm-app-devops?view=azureml-api-2
主题URL
在工作区之间使用Azure ML标准部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-connect-models-serverless?view=azureml-api-2
使用MLOpsPython将ML笔记本转换为生产脚本https://learn.microsoft.com/en-us/azure/machine-learning/how-to-convert-ml-experiment-to-production?view=azureml-api-1
将AutoML模型部署到AML在线端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-automl-endpoint?view=azureml-api-2
使用CLI v1将AML模型部署到Azure容器实例https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-container-instance?view=azureml-api-1
使用SDK/CLI v1将AML模型部署到Azure Kubernetes服务https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service?view=azureml-api-1
将自定义容器模型部署到AML在线端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-custom-container?view=azureml-api-2
在Azure ML Spark作业中运行MLflow模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-model-spark-jobs?view=azureml-api-2
在Azure ML在线端点上逐步推出MLflow模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-progressive?view=azureml-api-2
将MLflow模型部署到Azure ML端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models?view=azureml-api-2
在Azure ML中自定义批量部署输出https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-model-custom-output?view=azureml-api-2
将目录模型部署为Azure ML中的标准部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-serverless?view=azureml-api-2
将机器学习模型部署到Azure ML在线端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
将模型部署到AML托管在线端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
将模型部署到AML托管在线端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
将模型部署到AML托管在线端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
将模型部署到AML托管在线端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
将模型部署到AML托管在线端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
将Azure ML管道部署为批量端点https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-pipeline-component-as-batch-endpoint?view=azureml-api-2
在生产环境中发布和运行Azure ML管道https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-pipelines?view=azureml-api-1
在AML端点上使用NVIDIA Triton提供模型服务https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-triton?view=azureml-api-2
使用Azure DevOps构建Azure ML CI/CD管道https://learn.microsoft.com/en-us/azure/machine-learning/how-to-devops-machine-learning?view=azureml-api-2
为Azure ML CI/CD创建GitHub Actions工作流https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning?view=azureml-api-2
使用AML批量端点部署图像处理模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-image-processing-batch?view=azureml-api-2
使用Azure ML将MLflow模型部署为批量推理https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mlflow-batch?view=azureml-api-2
使用AML批量端点运行语言模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-nlp-processing-batch?view=azureml-api-2
通过已发布的管道重新训练Azure ML设计器模型https://learn.microsoft.com/en-us/azure/machine-learning/how-to-retrain-designer?view=azureml-api-1
使用VS Code本地运行Azure ML RAG prompt flowhttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-retrieval-augmented-generation-cloud-to-local?view=azureml-api-2
在Azure ML中部署和触发批量预测管道https://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer?view=azureml-api-1
为Azure ML在线端点执行安全的蓝绿发布https://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints?view=azureml-api-2
使用Azure DevOps和Azure ML设置端到端MLOpshttps://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-mlops-azureml?view=azureml-api-2
自动触发已发布的Azure ML管道https://learn.microsoft.com/en-us/azure/machine-learning/how-to-trigger-published-pipeline?view=azureml-api-1
使用AML批量端点部署模型以进行批量评分https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-model-deployments?view=azureml-api-2
通过AML批量端点运行Azure OpenAI嵌入https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-model-openai-embeddings?view=azureml-api-2
通过AML批量端点部署和调用管道https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-pipeline-deployments?view=azureml-api-2
将现有AML管道作业转换为批量端点部署https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-pipeline-from-job?view=azureml-api-2
在AML批量端点上运行评分管道https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-scoring-pipeline?view=azureml-api-2
在AML批量端点上运行训练管道https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-training-pipeline?view=azureml-api-2
使用Azure ML和prompt flow构建RAG管道https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-pipelines-prompt-flow?view=azureml-api-2
将prompt flow部署为托管在线端点以进行实时推理https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-deploy-for-real-time-inference?view=azureml-api-2
使用CLI将prompt flow部署到托管或Kubernetes在线端点https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-deploy-to-code?view=azureml-api-2
使用prompt flow和Azure DevOps管道实施GenAIOpshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-end-to-end-azure-devops-with-prompt-flow?view=azureml-api-2
使用prompt flow和GitHub管道实施GenAIOpshttps://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-end-to-end-llmops-with-prompt-flow?view=azureml-api-2
将prompt flow与DevOps管道集成以开发LLM应用https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-integrate-with-llm-app-devops?view=azureml-api-2