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Explore the essential components for creating reproducible machine learning experiments in this 33-minute conference talk. Learn how to combine Code (KubeFlow and Git), Data (Minio+lakeFS), and Environment (Infrastructure-as-code) to ensure true reproducibility. Witness a hands-on demonstration of reproducing an experiment while maintaining the exact input data, code, and processing environment from a previous run. Discover programmatic methods to integrate all aspects, including creating commits for data snapshots, tagging, and traversing the history of both code and data simultaneously. Gain insights into overcoming the limitations of MLFlow Projects in ensuring data reproducibility for comprehensive machine learning processes.
