In this tutorial, we will provide an in-depth and hands-on tutorial on Automated Machine Learning & Tuning with a fast python library FLAML. We will start with an overview of the AutoML problem and the FLAML library. In the first half of the tutorial, we will then give a hands-on tutorial on how to use FLAML to automate typical machine learning tasks in an end-to-end manner with different customization options and how to perform general tuning tasks on user-defined functions. In the second half of the tutorial, we will introduce several advanced functionalities of the library. For example, zero-shot AutoML, fair AutoML, and online AutoML. We will close the tutorial with several open problems, and challenges learned from AutoML practice.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this tutorial, we will provide an in-depth and hands-on tutorial on Automated Machine Learning & Tuning with a fast python library FLAML. We will start with an overview of the AutoML problem and the FLAML library. In the first half of the tutorial, we will then give a hands-on tutorial on how to use 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