Minimal REST Serving Layer for Keras Models With FastAPI

Added Jun 2025 3 design docs

Between a trained deep learning model and any product that uses it sits the same component every time: a serving layer that accepts requests, runs inference, and returns predictions. This project builds that component in its cleanest minimal form so every part of it is understood.

The intern builds a FastAPI project that loads a saved Keras model at startup and exposes a REST prediction endpoint. The work covers the serving fundamentals with care: structuring the application so the model loads once rather than on every request, defining typed request and response schemas so inputs are validated automatically, and shaping incoming JSON into the arrays the network expects. Worked example requests and responses document the contract precisely, and FastAPI's generated interactive docs make the endpoint testable from a browser — a pattern that applies to e-commerce recommendation calls as readily as to any other inference task.

The project distills model serving to its essentials so each concept lands clearly. The intern finishes able to put any Keras model behind a documented HTTP interface — the connective skill that makes deep learning usable by web and mobile applications.

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