This workshop provides a hands-on environment for participants to explore how to take machine learning systems from notebooks to production. Starting from classical DevOps foundations like version control, testing, and CI/CD, participants will learn how these practices extend to the ML lifecycle, covering experiment tracking, model versioning, and reproducible project structures. Building on these foundations, participants will design and implement deployment pipelines adapted to ML workflows, and learn how to keep production systems healthy.

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Given the fast-moving MLOps tooling landscape, the workshop content will be periodically updated to match current state of the art and best practices.

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