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Do you want to know the right way to do MLOps on Databricks? This course is for you!


Maria Vechtomova

MLOps Tech Lead | Databricks Beacon | 10+ years in Data & AI

Başak Eskili

Senior ML Engineer | 7+ years in Data & AI

End-to-end MLOps with Databricks

Implementing MLOps practices elevates data scientists and speeds up time to production. We've seen it through our careers. MLOps is not about what tools you use, it is about how you use them to follow MLOps principles. For any given machine learning model run/deployment in any environment, it must be possible to look up unambiguously: - corresponding code/commit on git;- infrastructure used for training and serving;- environment used for training and serving;- ML model artifacts;- what data was used to train the model. We teach you how to follow these principles using Databricks and develop on Databricks following the best software engineering practices. We spent the last 3 years working with Databricks and figuring it out with new features appearing all the time (such as Unity catalog, model serving, feature serving, Databricks Asset Bundles). It was not straightforward due to lacking documentation and notebook-first available training materials. In this course, we share all the knowledge we gained during our journey. Prerequisites: Python experience, basic knowledge of git, CI/CD.

Learn the right way to implement MLOps best practices on Databricks


Machine learning engineers who are familiar with MLOps but do not know how to do it on Databricks.

Machine learning engineers who are familiar with Databricks, but not familiar with the latest features.

Data scientists who work with Databricks, and want to know more about MLOps.

Who is this course for

Topics covered


<ul><li>MLOps toolbelt</li><li>Principles behind MLOps</li><li>Databricks MLOps components</li></ul>

MLOps principles and components


<ul><li>Developing in Python: best software development principles</li><li>Dbconnect &amp; VS code extension</li><li>Databricks Folders</li><li>From a notebook to production-ready code</li></ul>

Developing on Databricks


<ul><li>What is DAB?</li><li>Asset bundles components</li><li>Defining complex workflow in asset bundles</li><li>Using private packages in asset bundles</li></ul>

Databricks asset bundles (DAB)


<ul><li>Databricks'recommended approach</li><li>CI/CD pipeline with GitHub actions and Asset Bundles</li></ul>

Git branching strategy & Databricks environments


<ul><li>MLflow components</li><li>Track experiments &amp; search for experiments</li><li>Custom models in MLflow</li><li>Registering models in Unity Catalog</li></ul>

MLflow experiment tracking & registering models in Unity Catalog


<ul><li>Overview of architectures and use cases</li><li>Feature serving</li><li>Model serving (with automatic feature lookup)</li></ul>

Model serving architectures


<ul><li>What are inference tables</li><li>Setting up model evaluation pipeline</li><li>Data/model drift detection and lakehouse monitoring</li></ul>

Inference tables and lakehouse monitoring


Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

What people are saying

Meet your instructor

Live sessions where we walk you through the week's materials.

Wednesdays


Weekly projects

Course schedule

This course builds on live workshops and hands-on projects

Active hands-on learning

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

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