Explore the intricacies of tuning machine learning models in this 24-minute conference talk from Databricks. Delve into the automation of hyperparameter tuning, scaling techniques using Apache Spark, and best practices for optimizing workflows and architecture. Learn how to leverage Hyperopt, a popular open-source tool for ML tuning in Python, and discover its Spark-powered backend for enhanced scalability. Gain insights into effective tuning workflows, including how to select parameters, track progress, and iterate using MLflow. Examine architectural patterns for both single-machine and distributed ML workflows, and understand how to optimize data ingestion with Spark. Discover the potential of joblib-spark for distributing scikit-learn tuning jobs across Spark clusters. While generally accessible, this talk is particularly valuable for those with knowledge of machine learning and Spark.