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Serving ML Models with FastAPI and Docker
Putting a model behind an API is where many projects stall. This guide builds a FastAPI prediction service that loads a model once at start-up, enforces an input contract, serves single and batch predictions, reports health and readiness separately, and shows the Dockerfile that packages it.
Flask vs FastAPI vs Django: Which to Choose in 2026
Flask, FastAPI and Django compared on what actually differs: what each includes out of the box, how validation, async and data access work, the same small endpoint written in all three, and which one fits a content site, a JSON API, an ML service or an internal tool.
FastAPI Guide: Build a Production API Step by Step
A step-by-step FastAPI build: request validation with Pydantic models, response models that control what leaves the API, dependency injection, proper status codes and errors, tests with TestClient and dependency overrides, then the settings, workers and health checks a production deployment needs.