Model Serving (FastAPI/Flask)

Summary

Wrapping a trained model behind an HTTP API so other services can send it input and get predictions — the standard pattern for putting a model into production.

How to Learn This

1Wrap a trained model in a FastAPI endpoint and test it with real requests.
2Learn to handle input validation and batch prediction efficiently.
3Add basic error handling for malformed or out-of-distribution inputs.

Serverless AI Functions

Docker for ML Models

More topics in MLOps & Model Deployment

Docker for ML Models Model Versioning Experiment Tracking (MLflow, Weights & Biases)CI/CD for ML Pipelines Model Monitoring & Drift Detection A/B Testing Models Batch vs Real-time Inference Kubernetes for ML (Overview)Feature Stores (Overview)

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