This article presents a framework for versioning machine learning projects using Git branches. The framework aims to simplify workflows, organize data and models, and consolidate different aspects of the ML solution. It emphasizes the use of active branches for data, stable branches for training and inference, and coding branches for development. The goal is to improve workflow efficiency, governance, and collaboration in ML projects.
A Practical Approach to Versioning Machine Learning Projects using Git Branches
TL;DR
A simple approach to versioning machine learning projects using Git Branches that simplifies workflows, organizes data and models, and couples related parts of the project together.
Introduction
Managing machine learning solutions can be complex, with different aspects scattered across multiple platforms. This can lead to data loss, security breaches, and misconfigurations. In this article, we will show you how to use Git to simplify and organize your machine learning projects.
Key Concepts
– **Every change is a Git commit**: This includes data uploads, code changes, model overrides, and more.
– **Active branches**: Check out a branch and have all the necessary data, code, models, and documents in one place.
– **Your repository is your blob-store**: Use branches as buckets to upload, download, and store data and models.
– **Merges as workflow**: Combine branches to merge code and append data.
– **Deduplication**: Prevent the creation of multiple copies of files across branches.
Types of Branches
– **Main Branch**: Store problem definitions, documentation, and project structure. Use it for collaboration and tracking experiments.
– **Data Branches**: Include data files, documentation, and transformation scripts. Commit to the raw branch for a source of truth and create other branches for clean and split data.
– **Stable Branches**: Active branches for training and inference. Save models, checkpoints, and metrics. Tag commits for easy retrieval.
– **Coding Branches**: Develop code and explore data. Merge to stable branches when code changes are no longer required.
– **Monitoring Branch**: Save queried data, commit tags, and model predictions for monitoring purposes.
– **Analysis Branch**: Gather analysis code and non-training data for data science and analysis.
Summary
This article introduces a framework for versioning machine learning projects using Git branches. The framework simplifies workflows, organizes data and models, and couples related parts of the project together. It emphasizes the use of branches as environments, where each branch contains the necessary data, code, models, and documentation for a specific task. The framework aims to improve workflow efficiency, governance, and collaboration in machine learning projects.
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