Discover how to build a robust ML experimentation platform for easy reproducibility in this 36-minute conference talk from Data Council. Learn to leverage data versioning engines to intuitively version ML experiments and reproduce specific iterations. Follow along with a live code demonstration that covers creating a basic ML experimentation framework using lakeFS on Jupyter notebook, reproducing ML components from specific experiment iterations, and building an intuitive, zero-maintenance experiments infrastructure using common data engineering stacks and open-source tools. Gain insights from speaker Vino Duraisamy, a developer advocate at lakeFS with extensive experience in data management, batch processing, and MLOps from companies like NetApp, Nike, and Apple.
Overview
Syllabus
Building an ML Experimentation Platform for Easy Reproducibility | Treeverse
Taught by
Data Council
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