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Found 53 Skills
Use this skill to work with Microsoft Foundry (Azure AI Foundry): deploy AI models from catalog, build RAG applications with knowledge indexes, create and evaluate AI agents. USE FOR: Microsoft Foundry, AI Foundry, deploy model, model catalog, RAG, knowledge index, create agent, evaluate agent, agent monitoring. DO NOT USE FOR: Azure Functions (use azure-functions), App Service (use azure-create-app).
Integrate and optimize Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodelc, .mlpackage), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
Building and training neural networks with PyTorch. Use when implementing deep learning models, training loops, data pipelines, model optimization with torch.compile, distributed training, or deploying PyTorch models.
Comprehensive MLOps workflows for the complete ML lifecycle - experiment tracking, model registry, deployment patterns, monitoring, A/B testing, and production best practices with MLflow
Use when setting up, deploying, or operating vLLM Studio (env keys, controller/frontend startup, Docker services, branch workflow, and release checklists).
Integration templates for FastAPI endpoints, Next.js UI components, and Supabase schemas for ML model deployment. Use when deploying ML models, creating inference APIs, building ML prediction UIs, designing ML database schemas, integrating trained models with applications, or when user mentions FastAPI ML endpoints, prediction forms, model serving, ML API deployment, inference integration, or production ML deployment.
Select, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes. Use for model/backend/GPU/deployment-mode recipe bring-up; use router-starter for router-only mode work and troubleshoot for broken deployments.
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
MLflow, model versioning, experiment tracking, model registry, and production ML systems
Use this skill when deploying ML models to production, setting up model monitoring, implementing A/B testing for models, or managing feature stores. Triggers on model deployment, model serving, ML pipelines, feature engineering, model versioning, data drift detection, model registry, experiment tracking, and any task requiring machine learning operations infrastructure.
MosaicML integration. Manage data, records, and automate workflows. Use when the user wants to interact with MosaicML data.