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 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.
Note: This is the second course in the 'Machine Learning with Databricks’ series.
At a minimum, you should be familiar with the following before attempting to take this content:
- Knowledge of fundamental concepts in ML (e.g. regression vs classification models)
- Familiarity with the Databricks Workspace, Notebooks
- Intermediate-level knowledge of Python
- Basic knowledge of ML libraries (e.g. scikit-learn)
- Familiarity with Unity Catalog and MLflow is a plus
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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Public and private courses taught by expert instructors across half-day to two-day courses
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Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase
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Skills@Scale
Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details
