Databricks Academy offers the free Machine Learning Operations course to help machine learning practitioners understand how to manage models more effectively across the ML lifecycle on the Databricks Data Intelligence Platform.
As the fourth course in the โMachine Learning with Databricksโ series, it focuses on practical MLOps concepts, model lifecycle management, and the tools that support operational ML on Databricks.
Youโll learn to:
- Understand modern MLOps on Databricks: Learn how MLOps connects with DataOps, DevOps, and ModelOps to support more reliable machine learning workflows.
- Manage the model lifecycle more effectively: Explore how MLflow, Model Registry, and Unity Catalog help track, organize, and govern models across stages.
- Design and run practical MLOps workflows: Build a stronger foundation in setting up ML projects on Databricks using recommended tools and best practices.
- Monitor models and operations over time: Understand how monitoring, orchestration, and lifecycle tools help support model health and ongoing performance.
Designed for:
- ML practitioners who want a stronger foundation in MLOps and model lifecycle management
- Learners with basic Databricks and MLflow experience and familiarity with model development, deployment, and monitoring concepts
- Users comfortable with Python, workflow orchestration, and core MLOps fundamentals
Course format & details:
- Syllabus: 3 sections | 19 lessons
- Duration: About 2 hours
- Skill level: Associate
- Includes labs: No
- Cost: Free
๐ Enroll Now
