Model training

As Hamilton is a generic library for representing dataflows in pandas, it can be used for a wide array of tasks. One of the more common applications is using hamilton for training, testing, and executing machine learning models, all the way from feature-engineering through training and inference.

The following two examples show how to use Hamilton to model an entire ML pipeline:

  1. A classification pipeline for the iris dataset using scikit-learn

  2. An implementation of the m5 kaggle competition to do time-series forecasting on unit sales for using Walmart data.

The goal of these is to get you comfortable with building out ML pipelines using hamilton, potentially giving you inspiration/templates from which you can get started.