Serving Predictions with a FastAPI Endpoint
Learners will wrap a joblib model in a FastAPI POST route that accepts a JSON payload and returns a prediction, then test it with a curl request.
From Notebook to Production API
A Jupyter notebook is a great development environment but a terrible production serving system. The standard path from notebook to production is: train and save a model with joblib, wrap it in a REST API, and deploy that API as a containerised service. The API accepts raw feature values as JSON, preprocesses them through the fitted pipeline, and returns predictions in milliseconds.
Why FastAPI for ML Serving?
FastAPI is a modern Python web framework built on Pydantic and Starlette. It generates automatic interactive documentation (Swagger UI), validates request bodies with type hints, and handles async I/O efficiently. For ML serving, FastAPI is popular because it requires very little boilerplate, supports concurrent requests via async workers, and integrates naturally with Python data types used in sklearn and pandas.
All lessons in this course
- Saving Models with joblib and pickle
- Versioning Models: Why Filenames and Metadata Matter
- Serving Predictions with a FastAPI Endpoint
- Monitoring Predictions: Logging Inputs and Outputs