Reproducibility is a cornerstone of effective AI and Machine
Learning workflows. This lesson teaches learners how to implement version
control systems—specifically Git for code and DVC for datasets and
models—to ensure experiments can be reliably reproduced and shared among team
members.
Learners will explore practical strategies for setting up
repositories, committing changes systematically, managing datasets and model
versions, and rolling back to previous states when necessary. The lesson
emphasizes how proper version control improves collaboration, prevents errors,
and supports continuous experimentation and evaluation in AI/ML projects.
By the end of this session, learners will be able to
implement robust version control workflows that make AI/ML projects traceable,
reproducible, and maintainable, whether working individually or in a team
setting.
Key Learning Points
- Setting
up Git repositories and managing code versions - Using
DVC to track datasets and model files without bloating Git repositories - Creating
reproducible ML pipelines with combined Git + DVC workflows - Best
practices for committing, branching, and merging experiments - Ensuring
traceability and rollback capabilities for all project components
Practical Guidance
- Initialize
Git repository: Track all project code and configuration files. - Add
DVC tracking: Manage datasets and model artifacts efficiently. - Version
workflows: Commit changes regularly and use branches for experiments. - Reproduce
experiments: Use Git and DVC to roll back to previous experiment
states and validate results. - Collaboration:
Share repositories and DVC-tracked data for team-based reproducible
workflows.
