Automated Machine Learning (AutoML) has received significant interest recently. We believe that the right automation would bring significant value and dramatically shorten time-to-value for data science teams. Databricks is automating the Data Science and Machine Learning process through a combination of product offerings, partnerships, and custom solutions. This talk will focus on how Databricks can help automate hyperparameter tuning.

For both traditional Machine Learning and modern Deep Learning, tuning hyperparameters can dramatically increase model performance and improve training times. However, tuning can be a complex and expensive process. In this talk, we'll start with a brief survey of the most popular techniques for hyperparameter tuning (e.g., grid search, random search, and Bayesian optimization). We will then discuss open source tools that implement each of these techniques, helping to automate the search over hyperparameters.

Finally, we will discuss and demo improvements we built for these tools in Databricks, including integration with MLflow:

• Apache PySpark MLlib integration with MLflow for automatically tracking tuning
• Hyperopt integration with Apache Spark to distribute tuning and with MLflow for automatic tracking