MLflow: ML Lifecycle Management Platform
MLflow：机器学习生命周期管理平台
When to Use This Skill
何时使用该工具
Use MLflow when you need to:
- Track ML experiments with parameters, metrics, and artifacts
- Manage model registry with versioning and stage transitions
- Deploy models to various platforms (local, cloud, serving)
- Reproduce experiments with project configurations
- Compare model versions and performance metrics
- Collaborate on ML projects with team workflows
- Integrate with any ML framework (framework-agnostic)
Users: 20,000+ organizations | GitHub Stars: 23k+ | License: Apache 2.0
当你需要以下功能时，使用MLflow：
- 跟踪机器学习实验：记录参数、指标和产物
- 管理模型注册表：支持版本控制和阶段转换
- 部署模型：可部署到多种平台（本地、云端、服务端）
- 复现实验：通过项目配置复现实验结果
- 对比模型版本：比较不同版本的性能指标
- 团队协作：在机器学习项目中实现团队协作流程
- 框架兼容：可与任意机器学习框架集成（与框架无关）
用户规模：20,000+ 组织机构 | GitHub星标：23k+ | 许可证：Apache 2.0
Install MLflow
Install MLflow
Install with extras
Install with extras
pip install mlflow[extras] # Includes SQLAlchemy, boto3, etc.
pip install mlflow[extras] # Includes SQLAlchemy, boto3, etc.
Start MLflow UI
Start MLflow UI
with mlflow.start_run():
# Log parameters
mlflow.log_param("learning_rate", 0.001)
mlflow.log_param("batch_size", 32)
# Your training code
model = train_model()
# Log metrics
mlflow.log_metric("train_loss", 0.15)
mlflow.log_metric("val_accuracy", 0.92)
# Log model
mlflow.sklearn.log_model(model, "model")
with mlflow.start_run():
# Log parameters
mlflow.log_param("learning_rate", 0.001)
mlflow.log_param("batch_size", 32)
# Your training code
model = train_model()
# Log metrics
mlflow.log_metric("train_loss", 0.15)
mlflow.log_metric("val_accuracy", 0.92)
# Log model
mlflow.sklearn.log_model(model, "model")
Autologging (Automatic Tracking)
自动日志记录（Autologging）
python
import mlflow
from sklearn.ensemble import RandomForestClassifier
python
import mlflow
from sklearn.ensemble import RandomForestClassifier
Enable autologging
Enable autologging
Train (automatically logged)
Train (automatically logged)
model = RandomForestClassifier(n_estimators=100, max_depth=5)
model.fit(X_train, y_train)
model = RandomForestClassifier(n_estimators=100, max_depth=5)
model.fit(X_train, y_train)
Metrics, parameters, and model logged automatically!
Metrics, parameters, and model logged automatically!
1. Experiments and Runs
1. 实验与运行（Experiments and Runs）
Experiment: Logical container for related runs
Run: Single execution of ML code (parameters, metrics, artifacts)
Experiment（实验）：相关运行的逻辑容器
Run（运行）：机器学习代码的单次执行（包含参数、指标、产物）
Create/set experiment
Create/set experiment
mlflow.set_experiment("my-experiment")
mlflow.set_experiment("my-experiment")
with mlflow.start_run(run_name="baseline-model"):
# Log params
mlflow.log_param("model", "ResNet50")
mlflow.log_param("epochs", 10)
# Train
model = train()
# Log metrics
mlflow.log_metric("accuracy", 0.95)
# Log model
mlflow.pytorch.log_model(model, "model")
with mlflow.start_run(run_name="baseline-model"):
# Log params
mlflow.log_param("model", "ResNet50")
mlflow.log_param("epochs", 10)
# Train
model = train()
# Log metrics
mlflow.log_metric("accuracy", 0.95)
# Log model
mlflow.pytorch.log_model(model, "model")
Run ID is automatically generated
Run ID is automatically generated
print(f"Run ID: {mlflow.active_run().info.run_id}")
print(f"Run ID: {mlflow.active_run().info.run_id}")
2. Logging Parameters
2. 记录参数（Logging Parameters）
python
with mlflow.start_run():
# Single parameter
mlflow.log_param("learning_rate", 0.001)
# Multiple parameters
mlflow.log_params({
"batch_size": 32,
"epochs": 50,
"optimizer": "Adam",
"dropout": 0.2
})
# Nested parameters (as dict)
config = {
"model": {
"architecture": "ResNet50",
"pretrained": True
},
"training": {
"lr": 0.001,
"weight_decay": 1e-4
}
}
# Log as JSON string or individual params
for key, value in config.items():
mlflow.log_param(key, str(value))
python
with mlflow.start_run():
# Single parameter
mlflow.log_param("learning_rate", 0.001)
# Multiple parameters
mlflow.log_params({
"batch_size": 32,
"epochs": 50,
"optimizer": "Adam",
"dropout": 0.2
})
# Nested parameters (as dict)
config = {
"model": {
"architecture": "ResNet50",
"pretrained": True
},
"training": {
"lr": 0.001,
"weight_decay": 1e-4
}
}
# Log as JSON string or individual params
for key, value in config.items():
mlflow.log_param(key, str(value))
3. Logging Metrics
3. 记录指标（Logging Metrics）
python
with mlflow.start_run():
# Training loop
for epoch in range(NUM_EPOCHS):
train_loss = train_epoch()
val_loss = validate()
# Log metrics at each step
mlflow.log_metric("train_loss", train_loss, step=epoch)
mlflow.log_metric("val_loss", val_loss, step=epoch)
# Log multiple metrics
mlflow.log_metrics({
"train_accuracy": train_acc,
"val_accuracy": val_acc
}, step=epoch)
# Log final metrics (no step)
mlflow.log_metric("final_accuracy", final_acc)
python
with mlflow.start_run():
# Training loop
for epoch in range(NUM_EPOCHS):
train_loss = train_epoch()
val_loss = validate()
# Log metrics at each step
mlflow.log_metric("train_loss", train_loss, step=epoch)
mlflow.log_metric("val_loss", val_loss, step=epoch)
# Log multiple metrics
mlflow.log_metrics({
"train_accuracy": train_acc,
"val_accuracy": val_acc
}, step=epoch)
# Log final metrics (no step)
mlflow.log_metric("final_accuracy", final_acc)
4. Logging Artifacts
4. 记录产物（Logging Artifacts）
python
with mlflow.start_run():
# Log file
model.save('model.pkl')
mlflow.log_artifact('model.pkl')
# Log directory
os.makedirs('plots', exist_ok=True)
plt.savefig('plots/loss_curve.png')
mlflow.log_artifacts('plots')
# Log text
with open('config.txt', 'w') as f:
f.write(str(config))
mlflow.log_artifact('config.txt')
# Log dict as JSON
mlflow.log_dict({'config': config}, 'config.json')
python
with mlflow.start_run():
# Log file
model.save('model.pkl')
mlflow.log_artifact('model.pkl')
# Log directory
os.makedirs('plots', exist_ok=True)
plt.savefig('plots/loss_curve.png')
mlflow.log_artifacts('plots')
# Log text
with open('config.txt', 'w') as f:
f.write(str(config))
mlflow.log_artifact('config.txt')
# Log dict as JSON
mlflow.log_dict({'config': config}, 'config.json')
5. Logging Models
5. 记录模型（Logging Models）
import mlflow.pytorch
with mlflow.start_run():
model = train_pytorch_model()
mlflow.pytorch.log_model(model, "model")
import mlflow.pytorch
with mlflow.start_run():
model = train_pytorch_model()
mlflow.pytorch.log_model(model, "model")
import mlflow.sklearn
with mlflow.start_run():
model = train_sklearn_model()
mlflow.sklearn.log_model(model, "model")
import mlflow.sklearn
with mlflow.start_run():
model = train_sklearn_model()
mlflow.sklearn.log_model(model, "model")
Keras/TensorFlow
Keras/TensorFlow
import mlflow.keras
with mlflow.start_run():
model = train_keras_model()
mlflow.keras.log_model(model, "model")
import mlflow.keras
with mlflow.start_run():
model = train_keras_model()
mlflow.keras.log_model(model, "model")
HuggingFace Transformers
HuggingFace Transformers
import mlflow.transformers
with mlflow.start_run():
mlflow.transformers.log_model(
transformers_model={
"model": model,
"tokenizer": tokenizer
},
artifact_path="model"
)
import mlflow.transformers
with mlflow.start_run():
mlflow.transformers.log_model(
transformers_model={
"model": model,
"tokenizer": tokenizer
},
artifact_path="model"
)
Autologging
自动日志记录（Autologging）
Automatically log metrics, parameters, and models for popular frameworks.
Enable Autologging
启用自动日志记录
Enable for all supported frameworks
Enable for all supported frameworks
Or enable for specific framework
Or enable for specific framework
mlflow.sklearn.autolog()
mlflow.pytorch.autolog()
mlflow.keras.autolog()
mlflow.xgboost.autolog()
mlflow.sklearn.autolog()
mlflow.pytorch.autolog()
mlflow.keras.autolog()
mlflow.xgboost.autolog()
Autologging with Scikit-learn
Scikit-learn自动日志记录
python
import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
python
import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
Enable autologging
Enable autologging
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
Train (automatically logs params, metrics, model)
Train (automatically logs params, metrics, model)
with mlflow.start_run():
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42)
model.fit(X_train, y_train)
# Metrics like accuracy, f1_score logged automatically
# Model logged automatically
# Training duration logged
with mlflow.start_run():
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42)
model.fit(X_train, y_train)
# Metrics like accuracy, f1_score logged automatically
# Model logged automatically
# Training duration logged
Autologging with PyTorch Lightning
PyTorch Lightning自动日志记录
python
import mlflow
import pytorch_lightning as pl
python
import mlflow
import pytorch_lightning as pl
Enable autologging
Enable autologging
with mlflow.start_run():
trainer = pl.Trainer(max_epochs=10)
trainer.fit(model, datamodule=dm)
# Hyperparameters logged
# Training metrics logged
# Best model checkpoint logged
with mlflow.start_run():
trainer = pl.Trainer(max_epochs=10)
trainer.fit(model, datamodule=dm)
# Hyperparameters logged
# Training metrics logged
# Best model checkpoint logged
Model Registry
模型注册表（Model Registry）
Manage model lifecycle with versioning and stage transitions.
Log and register model
Log and register model
with mlflow.start_run():
model = train_model()
# Log model
mlflow.sklearn.log_model(
model,
"model",
registered_model_name="my-classifier" # Register immediately
)
with mlflow.start_run():
model = train_model()
# Log model
mlflow.sklearn.log_model(
model,
"model",
registered_model_name="my-classifier" # Register immediately
)
Or register later
Or register later
run_id = "abc123"
model_uri = f"runs:/{run_id}/model"
mlflow.register_model(model_uri, "my-classifier")
run_id = "abc123"
model_uri = f"runs:/{run_id}/model"
mlflow.register_model(model_uri, "my-classifier")
Model Stages
模型阶段（Model Stages）
Transition models between stages: None → Staging → Production → Archived
python
from mlflow.tracking import MlflowClient
client = MlflowClient()
在不同阶段之间转换模型：None（未分类） → Staging（测试） → Production（生产） → Archived（归档）
python
from mlflow.tracking import MlflowClient
client = MlflowClient()
Promote to staging
Promote to staging
client.transition_model_version_stage(
name="my-classifier",
version=3,
stage="Staging"
)
client.transition_model_version_stage(
name="my-classifier",
version=3,
stage="Staging"
)
Promote to production
Promote to production
client.transition_model_version_stage(
name="my-classifier",
version=3,
stage="Production",
archive_existing_versions=True # Archive old production versions
)
client.transition_model_version_stage(
name="my-classifier",
version=3,
stage="Production",
archive_existing_versions=True # Archive old production versions
)
Archive model
Archive model
client.transition_model_version_stage(
name="my-classifier",
version=2,
stage="Archived"
)
client.transition_model_version_stage(
name="my-classifier",
version=2,
stage="Archived"
)
Load Model from Registry
从注册表加载模型
python
import mlflow.pyfunc
python
import mlflow.pyfunc
Load latest production model
Load latest production model
model = mlflow.pyfunc.load_model("models:/my-classifier/Production")
model = mlflow.pyfunc.load_model("models:/my-classifier/Production")
Load specific version
Load specific version
model = mlflow.pyfunc.load_model("models:/my-classifier/3")
model = mlflow.pyfunc.load_model("models:/my-classifier/3")
Load from staging
Load from staging
model = mlflow.pyfunc.load_model("models:/my-classifier/Staging")
model = mlflow.pyfunc.load_model("models:/my-classifier/Staging")
predictions = model.predict(X_test)
predictions = model.predict(X_test)
Model Versioning
模型版本控制（Model Versioning）
python
client = MlflowClient()
python
client = MlflowClient()
List all versions
List all versions
versions = client.search_model_versions("name='my-classifier'")
for v in versions:
print(f"Version {v.version}: {v.current_stage}")
versions = client.search_model_versions("name='my-classifier'")
for v in versions:
print(f"Version {v.version}: {v.current_stage}")
Get latest version by stage
Get latest version by stage
latest_prod = client.get_latest_versions("my-classifier", stages=["Production"])
latest_staging = client.get_latest_versions("my-classifier", stages=["Staging"])
latest_prod = client.get_latest_versions("my-classifier", stages=["Production"])
latest_staging = client.get_latest_versions("my-classifier", stages=["Staging"])
Get model version details
Get model version details
version_info = client.get_model_version(name="my-classifier", version="3")
print(f"Run ID: {version_info.run_id}")
print(f"Stage: {version_info.current_stage}")
print(f"Tags: {version_info.tags}")
version_info = client.get_model_version(name="my-classifier", version="3")
print(f"Run ID: {version_info.run_id}")
print(f"Stage: {version_info.current_stage}")
print(f"Tags: {version_info.tags}")
Model Annotations
模型注释（Model Annotations）
python
client = MlflowClient()
python
client = MlflowClient()
Add description
Add description
client.update_model_version(
name="my-classifier",
version="3",
description="ResNet50 classifier trained on 1M images with 95% accuracy"
)
client.update_model_version(
name="my-classifier",
version="3",
description="ResNet50 classifier trained on 1M images with 95% accuracy"
)
client.set_model_version_tag(
name="my-classifier",
version="3",
key="validation_status",
value="approved"
)
client.set_model_version_tag(
name="my-classifier",
version="3",
key="deployed_date",
value="2025-01-15"
)
client.set_model_version_tag(
name="my-classifier",
version="3",
key="validation_status",
value="approved"
)
client.set_model_version_tag(
name="my-classifier",
version="3",
key="deployed_date",
value="2025-01-15"
)
Searching Runs
搜索运行记录（Searching Runs）
Find runs programmatically.
python
from mlflow.tracking import MlflowClient
client = MlflowClient()
通过编程方式查找运行记录。
python
from mlflow.tracking import MlflowClient
client = MlflowClient()
Search all runs in experiment
Search all runs in experiment
experiment_id = client.get_experiment_by_name("my-experiment").experiment_id
runs = client.search_runs(
experiment_ids=[experiment_id],
filter_string="metrics.accuracy > 0.9",
order_by=["metrics.accuracy DESC"],
max_results=10
)
for run in runs:
print(f"Run ID: {run.info.run_id}")
print(f"Accuracy: {run.data.metrics['accuracy']}")
print(f"Params: {run.data.params}")
experiment_id = client.get_experiment_by_name("my-experiment").experiment_id
runs = client.search_runs(
experiment_ids=[experiment_id],
filter_string="metrics.accuracy > 0.9",
order_by=["metrics.accuracy DESC"],
max_results=10
)
for run in runs:
print(f"Run ID: {run.info.run_id}")
print(f"Accuracy: {run.data.metrics['accuracy']}")
print(f"Params: {run.data.params}")
Search with complex filters
Search with complex filters
runs = client.search_runs(
experiment_ids=[experiment_id],
filter_string="""
metrics.accuracy > 0.9 AND
params.model = 'ResNet50' AND
tags.dataset = 'ImageNet'
""",
order_by=["metrics.f1_score DESC"]
)
runs = client.search_runs(
experiment_ids=[experiment_id],
filter_string="""
metrics.accuracy > 0.9 AND
params.model = 'ResNet50' AND
tags.dataset = 'ImageNet'
""",
order_by=["metrics.f1_score DESC"]
)
python
import mlflow
import torch
import torch.nn as nn
python
import mlflow
import torch
import torch.nn as nn
Enable autologging
Enable autologging
mlflow.pytorch.autolog()
with mlflow.start_run():
# Log config
config = {
"lr": 0.001,
"epochs": 10,
"batch_size": 32
}
mlflow.log_params(config)
# Train
model = create_model()
optimizer = torch.optim.Adam(model.parameters(), lr=config["lr"])
for epoch in range(config["epochs"]):
train_loss = train_epoch(model, optimizer, train_loader)
val_loss, val_acc = validate(model, val_loader)
# Log metrics
mlflow.log_metrics({
"train_loss": train_loss,
"val_loss": val_loss,
"val_accuracy": val_acc
}, step=epoch)
# Log model
mlflow.pytorch.log_model(model, "model")
mlflow.pytorch.autolog()
with mlflow.start_run():
# Log config
config = {
"lr": 0.001,
"epochs": 10,
"batch_size": 32
}
mlflow.log_params(config)
# Train
model = create_model()
optimizer = torch.optim.Adam(model.parameters(), lr=config["lr"])
for epoch in range(config["epochs"]):
train_loss = train_epoch(model, optimizer, train_loader)
val_loss, val_acc = validate(model, val_loader)
# Log metrics
mlflow.log_metrics({
"train_loss": train_loss,
"val_loss": val_loss,
"val_accuracy": val_acc
}, step=epoch)
# Log model
mlflow.pytorch.log_model(model, "model")
HuggingFace Transformers
HuggingFace Transformers
python
import mlflow
from transformers import Trainer, TrainingArguments
python
import mlflow
from transformers import Trainer, TrainingArguments
Enable autologging
Enable autologging
mlflow.transformers.autolog()
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=16,
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True
)
mlflow.transformers.autolog()
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=16,
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True
)
Start MLflow run
Start MLflow run
with mlflow.start_run():
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset
)
# Train (automatically logged)
trainer.train()
# Log final model to registry
mlflow.transformers.log_model(
transformers_model={
"model": trainer.model,
"tokenizer": tokenizer
},
artifact_path="model",
registered_model_name="hf-classifier"
)
with mlflow.start_run():
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset
)
# Train (automatically logged)
trainer.train()
# Log final model to registry
mlflow.transformers.log_model(
transformers_model={
"model": trainer.model,
"tokenizer": tokenizer
},
artifact_path="model",
registered_model_name="hf-classifier"
)
python
import mlflow
import xgboost as xgb
python
import mlflow
import xgboost as xgb
Enable autologging
Enable autologging
mlflow.xgboost.autolog()
with mlflow.start_run():
dtrain = xgb.DMatrix(X_train, label=y_train)
dval = xgb.DMatrix(X_val, label=y_val)
params = {
'max_depth': 6,
'learning_rate': 0.1,
'objective': 'binary:logistic',
'eval_metric': ['logloss', 'auc']
}
# Train (automatically logged)
model = xgb.train(
params,
dtrain,
num_boost_round=100,
evals=[(dtrain, 'train'), (dval, 'val')],
early_stopping_rounds=10
)
# Model and metrics logged automatically
mlflow.xgboost.autolog()
with mlflow.start_run():
dtrain = xgb.DMatrix(X_train, label=y_train)
dval = xgb.DMatrix(X_val, label=y_val)
params = {
'max_depth': 6,
'learning_rate': 0.1,
'objective': 'binary:logistic',
'eval_metric': ['logloss', 'auc']
}
# Train (automatically logged)
model = xgb.train(
params,
dtrain,
num_boost_round=100,
evals=[(dtrain, 'train'), (dval, 'val')],
early_stopping_rounds=10
)
# Model and metrics logged automatically
1. Organize with Experiments
1. 用实验组织项目
✅ Good: Separate experiments for different tasks
✅ Good: Separate experiments for different tasks
mlflow.set_experiment("sentiment-analysis")
mlflow.set_experiment("image-classification")
mlflow.set_experiment("recommendation-system")
mlflow.set_experiment("sentiment-analysis")
mlflow.set_experiment("image-classification")
mlflow.set_experiment("recommendation-system")
❌ Bad: Everything in one experiment
❌ Bad: Everything in one experiment
mlflow.set_experiment("all-models")
mlflow.set_experiment("all-models")
2. Use Descriptive Run Names
2. 使用描述性的运行名称
✅ Good: Descriptive names
✅ Good: Descriptive names
with mlflow.start_run(run_name="resnet50-imagenet-lr0.001-bs32"):
train()
with mlflow.start_run(run_name="resnet50-imagenet-lr0.001-bs32"):
train()
❌ Bad: No name (auto-generated UUID)
❌ Bad: No name (auto-generated UUID)
with mlflow.start_run():
train()
with mlflow.start_run():
train()
3. Log Comprehensive Metadata
3. 记录全面的元数据
python
with mlflow.start_run():
# Log hyperparameters
mlflow.log_params({
"learning_rate": 0.001,
"batch_size": 32,
"epochs": 50
})
# Log system info
mlflow.set_tags({
"dataset": "ImageNet",
"framework": "PyTorch 2.0",
"gpu": "A100",
"git_commit": get_git_commit()
})
# Log data info
mlflow.log_param("train_samples", len(train_dataset))
mlflow.log_param("val_samples", len(val_dataset))
python
with mlflow.start_run():
# Log hyperparameters
mlflow.log_params({
"learning_rate": 0.001,
"batch_size": 32,
"epochs": 50
})
# Log system info
mlflow.set_tags({
"dataset": "ImageNet",
"framework": "PyTorch 2.0",
"gpu": "A100",
"git_commit": get_git_commit()
})
# Log data info
mlflow.log_param("train_samples", len(train_dataset))
mlflow.log_param("val_samples", len(val_dataset))
4. Track Model Lineage
4. 跟踪模型 lineage
Link runs to understand lineage
Link runs to understand lineage
with mlflow.start_run(run_name="preprocessing"):
data = preprocess()
mlflow.log_artifact("data.csv")
preprocessing_run_id = mlflow.active_run().info.run_id
with mlflow.start_run(run_name="training"):
# Reference parent run
mlflow.set_tag("preprocessing_run_id", preprocessing_run_id)
model = train(data)
with mlflow.start_run(run_name="preprocessing"):
data = preprocess()
mlflow.log_artifact("data.csv")
preprocessing_run_id = mlflow.active_run().info.run_id
with mlflow.start_run(run_name="training"):
# Reference parent run
mlflow.set_tag("preprocessing_run_id", preprocessing_run_id)
model = train(data)
5. Use Model Registry for Deployment
5. 使用模型注册表进行部署
✅ Good: Use registry for production
✅ Good: Use registry for production
model_uri = "models:/my-classifier/Production"
model = mlflow.pyfunc.load_model(model_uri)
model_uri = "models:/my-classifier/Production"
model = mlflow.pyfunc.load_model(model_uri)
❌ Bad: Hard-code run IDs
❌ Bad: Hard-code run IDs
model_uri = "runs:/abc123/model"
model = mlflow.pyfunc.load_model(model_uri)
model_uri = "runs:/abc123/model"
model = mlflow.pyfunc.load_model(model_uri)
Serve Model Locally
本地部署模型
Serve registered model
Serve registered model
mlflow models serve -m "models:/my-classifier/Production" -p 5001
mlflow models serve -m "models:/my-classifier/Production" -p 5001
Serve from run
Serve from run
mlflow models serve -m "runs:/<RUN_ID>/model" -p 5001
mlflow models serve -m "runs:/<RUN_ID>/model" -p 5001
Test endpoint
Test endpoint
Deploy to AWS SageMaker
Deploy to AWS SageMaker
mlflow sagemaker deploy -m "models:/my-classifier/Production" --region-name us-west-2
mlflow sagemaker deploy -m "models:/my-classifier/Production" --region-name us-west-2
Deploy to Azure ML
Deploy to Azure ML
mlflow azureml deploy -m "models:/my-classifier/Production"
mlflow azureml deploy -m "models:/my-classifier/Production"
Tracking Server
跟踪服务器（Tracking Server）
Start tracking server with backend store
Start tracking server with backend store
mlflow server
--backend-store-uri postgresql://user:password@localhost/mlflow
--default-artifact-root s3://my-bucket/mlflow
--host 0.0.0.0
--port 5000
mlflow server
--backend-store-uri postgresql://user:password@localhost/mlflow
--default-artifact-root s3://my-bucket/mlflow
--host 0.0.0.0
--port 5000
Client Configuration
客户端配置
Set tracking URI
Set tracking URI
Or use environment variable
Or use environment variable
- - Comprehensive tracking guide
references/model-registry.md
- - Production deployment patterns
- - 全面的跟踪指南
references/model-registry.md
- - 生产部署模式