MLFLow plugin for Hamilton

MLFlow is an open-source Python framework for experiment tracking. It allows data science teams to store results, artifacts (machine learning models, figures, tables), and metadata in a principled way when executing data pipelines.

The MLFlow plugin for Hamilton includes two sets of features:

Save and load machine learning models with the MLFlowModelSaver and MLFlowModelLoader materializers
Automatically track data pipeline results in MLFlow with the MLFlowTracker.

This pairs nicely with the HamiltonTracker and the Hamilton UI which gives you a way to explore your pipeline code, attributes of the artifacts produced, and execution observability.

We're working on better linking Hamilton “projects” with MLFlow “experiments” and runs from both projects.

Instructions

Create a virtual environment and activate it

python -m venv venv && . venv/bin/active

Install requirements for the Hamilton code
```
pip install -r requirements.txt
```
Explore the notebook tutorial.ipynb
Launch the MLFlow user interface to explore results
```
mlflow ui
```

Going further

Learn the basics of Hamilton via the Concepts/ documentation section
Visit tryhamilton.dev for an interactive tutorial in your browser
Visit the DAGWorks blog for more detailed guides