Flask is lightweight and perfect for serving machine learning models as REST APIs. Moving from a Jupyter notebook to a production Flask service requires attention to performance, security, and reliability.
Production Server Setup
Never use Flask's built-in development server in production. Deploy with Gunicorn or uWSGI behind Nginx as a reverse proxy. Containerize with Docker for consistent environments across staging and production.
Model Loading Strategy
Load ML models once at application startup, not on every request. Use lazy loading for large models and consider model versioning so you can roll back without downtime.
API Design for ML
- Validate input dimensions and data types strictly
- Return confidence scores alongside predictions
- Set request timeouts appropriate for inference time
- Monitor latency, error rates, and model drift
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