Version control is a critical component of modern AI and
Machine Learning workflows. This lesson introduces learners to Git, the
most widely used tool for tracking changes in code, and Data Version Control
(DVC), which extends versioning to datasets and models. By integrating
these tools into AI/ML projects, practitioners can maintain reproducible
experiments, collaborate effectively, and ensure traceability throughout the
model lifecycle.
Learners will explore best practices for setting up
repositories, committing changes, tracking large datasets and models with DVC,
and creating a workflow that supports collaboration and experiment tracking.
The lesson also emphasizes reproducibility, showing how version control
allows teams to roll back to previous states and compare different iterations
of code, data, and model outputs.
By the end of this session, learners will understand the
purpose and advantages of version control in AI/ML workflows and be able to
apply practical strategies to implement it in real-world projects.
Key Learning Points
- Overview
of Git: commits, branches, and collaboration workflows - Introduction
to DVC for versioning datasets and ML models - Setting
up reproducible AI/ML experiments - Best
practices for integrating Git and DVC in project pipelines - Benefits
of traceability, rollback, and collaboration in team environments
Practical Example / Guidance
- Git:
Initialize a repository, commit changes, create branches, and merge
features. - DVC:
Track datasets and model files without bloating Git repositories. - Workflow:
Combine Git for code and DVC for data/models to maintain fully
reproducible pipelines.
