Model Deployment

Deploying ML Models

Putting models into production enables real-world use.

Serialization

Pickle: joblib.dump(model, 'model.pkl'). Saves entire model object.

ONNX provides framework-agnostic format. Converts models for deployment elsewhere.

API Creation

Flask: @app.route('/predict', methods=['POST']). Load model, process input, return prediction.

FastAPI provides async support and automatic documentation.

Containerization

Docker packages model with dependencies. Ensure consistent environments.

Use requirements.txt or environment.yml to specify packages.

Key Takeaways

1. Serialize models for reuse
2. Flask/FastAPI create prediction APIs
3. Docker ensures reproducible deployment