How to Track ML Model Training: Scikit-learn + Integration

1 min
Prince Canuma
5th April, 2022

In this video, we show how to keep track of your model-training metadata when using scikit-learn. Prince Canuma explains: 

  • How to use the Neptune-scikit-learn (sklearn) integration
  • How to log your sklearn training metadata to Neptune (including regressor parameters, pickled model, test predictions, test scores, and more),
  • How to analyze the data in the Neptune app, 
  • And more. 

Important: This video was created in December 2021. For the most up-to-date tutorials and examples, please refer to the Neptune-scikit-learn integration docs

If you want to try out the integration on your own, check this Neptune-Scikit learn Colab notebook or check this Neptune-Scikit-learn GitHub repo. is an ML metadata store primarily used for experiment tracking and model registry

We are 100% focused on ML metadata management, but we’re making sure that it’s easy to integrate Neptune with other components of the ML stack. 

Neptune is already integrated with 25+ tools and libraries, and the list is growing. You can check our roadmap to see what’s currently under development.  

Other useful resources

See a Scikit-learn example project in the Neptune app (no registration needed). 

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