Module 2: Version Control for ML
๐ฏ Learning Objectivesโ
By the end of this module, you will be able to:
- Apply Git best practices specifically for ML projects
- Implement Data Version Control (DVC) for tracking datasets and models
- Design and implement model versioning strategies with registries
- Create reproducible ML pipelines with proper lineage tracking
๐ Module Contentโ
Chapter 1: Git for ML Projectsโ
Best practices for version control in ML, handling large files, proper .gitignore configuration, and branching strategies.
Chapter 2: Data Version Control (DVC)โ
Setting up DVC, tracking data and models, remote storage configuration, and building pipelines.
Chapter 3: Model Versioningโ
Model registries, versioning strategies, metadata tracking, and model lineage.
โฑ๏ธ Estimated Timeโ
| Activity | Duration |
|---|---|
| Reading | 2.5 hours |
| Hands-on Exercises | 2 hours |
| Quiz | 30 minutes |
| Total | 5 hours |
๐ Key Conceptsโ
๐ ๏ธ Tools Coveredโ
| Tool | Purpose |
|---|---|
| Git | Code version control |
| DVC | Data and model versioning |
| MLflow Model Registry | Model management |
| Git LFS | Large file handling |
๐ Let's Begin!โ
Start with Chapter 1: Git for ML Projects to learn ML-specific Git practices.