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Overview
This comprehensive course provides a practical guide to developing traditional machine learning models on Databricks, emphasizing hands-on demonstrations and workflows using popular ML libraries. Participants will explore key ML techniques, including regression and clustering, while leveraging Databricks’ powerful capabilities. The course covers MLflow integration for model tracking, Databricks Feature Store for feature management, and Optuna for hyperparameter tuning. Additionally, participants will learn how to accelerate model training with Databricks AutoML. By the end of the course, learners will have real-world, practical skills to develop, optimize, and deploy machine learning models efficiently in the Databricks environment.
Model Development and MLflow Evaluating Model Performance Hyperparameter Tuning Fundamentals Hyperparameter Tuning with Hyperopt Automated Model Development with AutoML
At a minimum, you should be familiar with the following before attempting to take this content:
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