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Greg Michaelson AutoML as it should have always been | JupyterCon 2023
Pipelines: A revolutionary open-source AutoML package that addresses the limitations of existing tools by providing a transparent, flexible, and easy-to-use solution for developing and deploying machine learning models.
- Automated machine learning (AutoML) tools have been around for a while, but they have several limitations.
- AutoML tools often only work on a subset of problems, are opaque, and require users to accept a black box solution.
- Pipelines is a new open-source package that aims to address these limitations by providing a more transparent and flexible AutoML solution.
- Pipelines can be used to generate code for training, tuning, and evaluating machine learning models.
- Pipelines supports supervised learning for regression and classification tasks and includes a variety of models, including elastic net, random forest, decision tree, and gradient boosting.
- Pipelines can be used to parallelize training and tuning tasks, which can significantly reduce the time it takes to develop and deploy machine learning models.
- Pipelines is easy to use and can be integrated with Jupyter notebooks and other development environments.
- Pipelines is still under development, but it has the potential to revolutionize the way that machine learning models are developed and deployed.