Automated Machine Learning & Tuning with FLAML

  • Chi Wang ,
  • Qingyun Wu ,
  • Xueqing Liu ,
  • Luis Quintanilla

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) |

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.

Publication Downloads

FLAML: A Fast Library for AutoML and Tuning

December 15, 2020

FLAML is a Python library designed to automatically produce accurate machine learning models with low computational cost. It frees users from selecting learners and hyperparameters for each learner. FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection method invented by Microsoft Research.