Databricks Academy offers the free Advanced Machine Learning Operations course to help machine learning practitioners understand how to run ML projects more reliably at scale on Databricks.
As the second course in the Advanced Machine Learning series, it focuses on practical MLOps topics such as CI/CD, testing, monitoring, deployment strategies, and multi-environment management.
You’ll learn to:
- Understand the full MLOps lifecycle: Build a stronger foundation in machine learning operations, including data and model management, testing, and scalable ML architectures.
- Use Databricks tools for ML automation: Learn how tools like Databricks Asset Bundles, Workflows, and Mosaic AI Model Serving support automation and infrastructure management.
- Monitor model quality and reliability: Explore custom metrics, drift detection, rollout strategies, A/B testing, and Lakehouse Monitoring to support healthier production models.
- Manage deployments across environments: Understand CI/CD, pipeline management, environment separation, and the role of DABs in streamlining ML deployments.
Designed for:
- Machine learning practitioners building and operating ML systems on Databricks
- Users with intermediate experience in Python, Git, MLflow, and Unity Catalog
- Learners familiar with model development, deployment concepts, Workflows, and CI/CD basics
Course format & details:
- Series: Second course in the Advanced Machine Learning series
- Syllabus: 5 sections | 21 lessons
- Duration: 2 hours
- Skill level: Professional
- Cost: Free
- Includes labs: No
Technical considerations:
- Runtime: DBR ML 15.4
- Workspace requirements: Unity Catalog and Model Serving enabled
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