Machine Learning Model Development
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 development with Genie Code, Databricks' AI-powered coding assistant that uses natural language, MCP connections, instructions, and skills to guide the full ML lifecycle. 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.
Note:
- This is the second course in the 'Machine Learning with Databricks’ series.
- Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!
At a minimum, you should be familiar with the following before attempting to take this content:
• Familiarity with the Databricks Data Intelligence Platform and basic workspace operations (create clusters, run code in notebooks, use basic notebook operations, import repos from git)
• Intermediate programming experience with Python, including data manipulation libraries (pandas, numpy) and working with APIs (databricks-sdk, REST endpoints)
• Basic knowledge of MLflow for experiment tracking, model logging, model registry operations, and model versioning
• Understanding of machine learning fundamentals, including model training, evaluation, batch inference, and real-time deployment concepts
• Intermediate experience with Unity Catalog for data governance and model registry management
• Basic familiarity with Feature Engineering concepts, including feature tables, feature lookups, and offline vs online feature stores
• Understanding of Delta Lake operations (create tables, perform updates, optimize files, and liquid clustering) and data storage optimization techniques
• Basic knowledge of Apache Spark and PySpark for distributed data processing and User Defined Functions (UDFs)
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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