Databricks Academy offers the free Machine Learning Model Development course to help machine learning practitioners build, tune, and improve traditional machine learning models on the Databricks Data Intelligence Platform.
As the second course in the โMachine Learning with Databricksโ series, it focuses on practical workflows for model development using popular ML libraries and Databricks-native tools.
Youโll learn to:
- Build machine learning models on Databricks: Learn the core workflow for developing traditional ML models, including common techniques such as regression and clustering in the Databricks environment.
- Track and manage experiments with MLflow: Use MLflow to log runs, track model performance, and manage model development more effectively from experimentation to iteration.
- Tune models for better performance: Understand hyperparameter tuning and use Optuna to improve model performance in a more structured and efficient way.
- Speed up development with AutoML: Use Databricks AutoML through the UI and API to run experiments, compare models, and build on generated results faster.
Designed for:
- ML practitioners who want hands-on experience building and tuning models on Databricks
- Learners with basic Databricks and MLflow experience and familiarity with model training, evaluation, and deployment concepts
- Users comfortable with Python, Spark, Delta Lake, Unity Catalog, and feature engineering fundamentals
Course format & details:
- Syllabus: 3 sections | 18 lessons
- Duration: About 2 hours
- Skill level: Associate
- Includes labs: No
- Cost: Free
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