Hyperparameter Tuning with Hyperopt on Databricks

Performing effective model hyperparameter tuning is a complex process, that involves keeping track of several trained candidate models and their evaluation metrics and training datasets. Databricks comes with excellent built-in support for this via its Hyperopt and MLflow integrations.In this course, learn about the Databricks platform and its capabilities for machine learning (ML) optimization. Next, discover how to create an Azure Databricks workspace, launch an Apache Spark cluster, create a Databricks volume for CSV data, and write Python code in Databricks Notebooks for model and hyperparameter tuning. Finally, explore how to track your tuning experiments using MLflows in Databricks and perform distributed hyperparameter tuning with SparkTrials.After completing this video, you will be able to perform hyperparameter tuning with Hyperopt on Databricks.