Hyperparameter tuning and optimization is a powerful tool in the area of AutoML, for both traditional statistical learning models as well as for deep learning. There are many existing tools to help drive this process, including both blackbox and whitebox tuning. In this talk, we’ll start with a brief survey of the most popular techniques for hyperparameter tuning (e.g., grid search, random search, Bayesian optimization, and parzen estimators) and then discuss the open source tools which implement each of these techniques. Finally, we will discuss how we can leverage MLflow with these tools and techniques to analyze how our search is performing and to productionize the best models.
Joseph Bradley works as a Sr. Solutions Architect at Databricks, specializing in Machine Learning, and is an Apache Spark committer and PMC member. Previously, he was a Staff Software Engineer at Databricks and a postdoc at UC Berkeley, after receiving his Ph.D. in Machine Learning from Carnegie Mellon.