1. Introduction to Version Control for Machine Learning

1.1 Importance of version control in machine learning projects
1.2 Overview of challenges in managing machine learning models and experiments
1.3 Benefits of using Git for tracking machine learning workflows
1.4 Course objectives and learning outcomes

2. Setting Up Git for Machine Learning Projects

2.1 Initializing a Git repository for machine learning projects
2.2 Organizing project structure for code, data, and model artifacts
2.3 Tracking datasets and training scripts in Git repositories
2.4 Setting up Git for team collaboration in machine learning environments

3. Versioning Machine Learning Code and Scripts

3.1 Best practices for versioning machine learning code
3.2 Using Git branches and commits for model iteration
3.3 Implementing feature branches for machine learning experiments
3.4 Documenting changes and improvements in model performance

4. Tracking and Versioning Machine Learning Models

4.1 Techniques for versioning trained machine learning models with Git
4.2 Storing model weights and parameters efficiently in Git repositories
4.3 Handling large model files and external storage options (e.g., Git LFS)
4.4 Using tags and releases for significant model versions and deployments

5. Managing Datasets with Git

5.1 Strategies for versioning datasets in machine learning workflows
5.2 Handling large datasets in Git repositories
5.3 Integrating Git Large File Storage (LFS) for managing datasets and artifacts
5.4 Tracking dataset changes and ensuring reproducibility across experiments

6. Automating Machine Learning Workflows with Git

6.1 Automating training, testing, and deployment workflows using Git
6.2 Setting up continuous integration (CI) for machine learning projects
6.3 Using Git hooks for automatic testing and validation of models
6.4 Git-based tools for tracking machine learning experiments (DVC, MLflow)

7. Collaborative Machine Learning Model Development

7.1 Collaborating on machine learning model development with Git
7.2 Using GitHub Issues and Projects for tracking experiments and tasks
7.3 Pull requests and code reviews for machine learning projects
7.4 Best practices for team collaboration and version control in machine learning

8. Ensuring Reproducibility in Machine Learning

8.1 Ensuring reproducibility of machine learning experiments with Git
8.2 Using Git for tracking dependencies and environment configurations
8.3 Managing Jupyter Notebooks and ML pipelines in Git repositories
8.4 Ensuring consistent results across different versions of datasets and models

9. Case Study: Managing Machine Learning Models with Git

9.1 Real-world case study on using Git for machine learning model development
9.2 Analyzing the challenges and solutions in model versioning
9.3 Lessons learned from industry practices and Git-based workflows
9.4 Key takeaways for applying Git to ML version control in practice

10. Final Project: Implementing Git for ML Model Versioning

10.1 Setting up a machine learning project with full Git integration
10.2 Tracking code, datasets, and models with version control
10.3 Collaborating with team members on model development and experiment tracking
10.4 Presenting and reviewing the final project using Git workflows

11. Conclusion and Next Steps

11.1 Recap of best practices for using Git in machine learning
11.2 Tools and resources for advanced ML version control and collaboration
11.3 Future trends in version control for machine learning projects
11.4 Additional learning paths and career development with Git for ML

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Reference for Git

Reference for Machine Learning