In this article, you will learn how to build a robust JSON serialization pipeline in Python that handles custom data types, graceful error recovery, and schema migrations.

Topics we will cover include:

How to define a custom JSON encoder to handle Python types like datetime , set , Path , and Enum .

How to write a custom decoder function that reconstructs those original Python objects from a serialized JSON string, including graceful handling of malformed data.

How to manage schema changes over time using a factory function that applies defaults and migrations during deserialization.

Introduction and Motivation

Anyone fairly experienced in constructing machine learning pipelines might have noticed at some stage that only a small percentage of their time is dedicated to writing the model architecture. What do they spend the rest of that time on?…