Machine Learning Operations
Databricks
Last updated June 30, 2026
Machine Learning Operations is a free, two-hour course on MLOps — the practices that keep machine learning models reliable across their whole lifecycle, not just at the moment they're trained. It covers managing a model from development through to ongoing operation: the processes and discipline that stop a system degrading silently once it's in use. It assumes Python and basic machine learning knowledge, and the lifecycle-management workflows are taught on the Databricks platform. It's a sensible next step once you can build and deploy a model and want to operate one properly.
What you'll learn
- What MLOps is and why model reliability needs ongoing operations
- Managing a model across its full lifecycle, from development to live use
- The processes that stop a deployed model degrading unnoticed
- Practical lifecycle-management workflows
- Applying them on the Databricks platform
Frequently asked questions about Machine Learning Operations
Who is Machine Learning Operations for?
Developers who can build and deploy machine learning models and want to learn the MLOps practices that keep them reliable over their lifecycle.
Is Machine Learning Operations free?
Yes — Machine Learning Operations is completely free to take.
What are the prerequisites for Machine Learning Operations?
Python; basic machine learning; familiarity with Databricks. A free Databricks Academy account is required to start.
Do you need to code for Machine Learning Operations?
Yes — Machine Learning Operations involves hands-on coding.
Why we suggest this course
Introduces the operational discipline that keeps models reliable in the long run, for developers who can build and deploy but haven't yet thought about the full lifecycle. One thing to know: the MLOps workflows are taught on Databricks and need a free Academy account, so the practices are framed around that toolset.