Automated Machine Learning (AutoML) has received significant interest recently because of its ability to shorten time-to-value for data science teams and maximize predictive performance. However, getting to this ideal state can be a complex and resource-intensive process.
In this talk, we'll review the most popular techniques for hyperparameter tuning and dive into several open source tools that implement each of these techniques. 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
Recording and notebooks will be provided after the webinar so that you can practice at your own pace.