Deploy ML Models to Production

Step 1: Package your model with MLflow

Log your trained model to MLflow with all dependencies and metadata. This ensures reproducibility and version control.

import mlflow
mlflow.sklearn.log_model(model, "model")

Step 2: Create a FastAPI service

Build a REST API wrapper around your model using FastAPI. Include health checks, input validation, and error handling.

Step 3: Implement model versioning

Set up a system to track model versions and their performance metrics. Enable easy rollback to previous versions.

Step 4: Add A/B testing infrastructure

Implement a framework to test new model versions against production. Route traffic and compare performance metrics.

Step 5: Set up monitoring and alerts

Monitor model performance, prediction latency, and data drift. Configure alerts for performance degradation or anomalies.