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Building Reproducible ML Processes with an Open Source Stack

Offered By: Linux Foundation via YouTube

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Machine Learning Courses Git Courses Infrastructure as Code Courses MLFlow Courses Kubeflow Courses Experiment Tracking Courses Minio Courses LakeFS Courses

Course Description

Overview

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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.

Syllabus

Building Reproducible ML Processes with an Open Source Stack - Einat Orr, Treeverse


Taught by

Linux Foundation

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