Model Registry
Centralizing model management:
Model Registry Functions:
- Model versioning
- Metadata storage
- Artifact management
- Lineage tracking
- Deployment management
- Approval workflows
Example Model Registry Implementation:
# MLflow Model Registry example
import mlflow
from mlflow.tracking import MlflowClient
# Initialize client
client = MlflowClient()
# Register model from run
run_id = "abcdef123456"
model_uri = f"runs:/{run_id}/model"
model_name = "customer_churn_predictor"
# Register model in registry
model_details = mlflow.register_model(model_uri, model_name)
model_version = model_details.version
# Add model description
client.update_model_version(
name=model_name,
version=model_version,
description="Random Forest model trained on customer data from Q1 2025"
)
# Add model tags
client.set_model_version_tag(
name=model_name,
version=model_version,
key="data_version",
value="v2.1"
)
# Transition model to staging
client.transition_model_version_stage(
name=model_name,
version=model_version,
stage="Staging"
)
# After validation, transition to production
client.transition_model_version_stage(
name=model_name,
version=model_version,
stage="Production"
)
Model Registry Best Practices:
- Implement model approval workflows
- Track model lineage and dependencies
- Store model performance metrics
- Link models to training data
- Document model limitations
- Implement access controls
ML Platforms
Unified environments for ML development and deployment:
ML Platform Components:
- Notebook environments
- Training infrastructure
- Feature stores
- Model registries
- Deployment services
- Monitoring tools
Popular ML Platforms:
- Kubeflow
- MLflow
- SageMaker
- Vertex AI
- Azure ML
- Databricks
ML Platform Selection Criteria:
- Scalability requirements
- Integration with existing tools
- Support for preferred frameworks
- Governance capabilities
- Cost considerations
- Team expertise
ML Governance and Compliance
Model Governance
Ensuring responsible ML practices:
Model Governance Components:
- Model documentation
- Explainability methods
- Bias detection and mitigation
- Compliance validation
- Audit trails
- Risk assessment
Example Model Card:
# Model Card: Customer Churn Prediction
## Model Details
- **Model Name**: customer_churn_predictor_v2
- **Version**: 2.0.0
- **Type**: Random Forest Classifier
- **Framework**: scikit-learn 1.2.0