Having established how to create endpoints, handle data with Pydantic, and integrate machine learning models, we now turn our attention to building more maintainable and reliable applications. As your ML APIs grow in complexity, proper organization and testing become important for sustainability.

This chapter provides guidance on structuring your FastAPI project effectively and implementing testing strategies. You will learn how to use APIRouter to modularize your code, apply principles for separating concerns, manage project dependencies, and write unit and integration tests using FastAPI's TestClient. We will also cover logging practices and methods for handling application configuration and secrets. The goal is to equip you with the techniques needed for developing scalable and dependable ML deployment services.