A comprehensive guide to structuring Python ML projects for production MLOps systems using FastAPI. Covers the src/ layout pattern, dependency management with Poetry/PDM/UV, layered configuration using Pydantic Settings and YAML, structured logging best practices, FastAPI endpoint design with health checks and prediction
Table of contents
FastAPI for MLOps: Python Project Structure and API Best PracticesIntroductionPython Project Structure Best Practices for MLOpsManaging Python Dependencies with Poetry for ML ProjectsConfiguration Management in MLOps: YAML, .env, and PydanticLogging Best Practices for MLOps and FastAPI ApplicationsFastAPI for MLOps: Building a Production ML APIMLOps Architecture: Service Layer Design PatternsModel Abstraction in MLOps: Decoupling ML from APIsBuilding Reusable Utilities in Python MLOps ProjectsRunning a FastAPI MLOps Application LocallySummarySort: