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.