Hamilton: Micro-framework for Dataflows

As AI applications grow, their data pipelines often become a "spaghetti" of dependencies. Hamilton is a micro-framework developed at Stitch Fix that forces you to define your dataflow as a collection of simple Python functions, where the function name and arguments define the dependency graph.

Lineage and Documentation

Because the graph is defined by code, Hamilton provides automatic lineage tracking and documentation. You can visualize exactly how a piece of data was calculated, which is essential for debugging complex feature engineering or data preprocessing steps in machine learning models.

Unit-Testable Pipelines

By breaking your pipeline into small, pure functions, Hamilton makes your data logic naturally unit-testable. This modularity ensures that your AI data pipelines are robust, maintainable, and easy to share across a team of data scientists and engineers.