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.
neptune.ai 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).
More about How to Track ML Model Training: Scikit-learn + neptune.ai Integration
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Model Training: Detectron2 + neptune.ai Integration [Example]
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Model Training: Prophet + neptune.ai Integration [Example]
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Create AzureML Pipeline – Workshop with Aurimas Griciūnas
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