With Gaurav Sood

The first part of the series, “Improving and Deploying On-Device Models With Confidence,” is posted here.

Tracking Changes in ML Systems

ML Engineers spend a lot of time keeping track of changes. The changes can be to the model architecture, hyperparameters, training data, or the deployment infrastructure. Each of these changes can affect the model’s performance in ways that are hard to predict. And when something goes wrong, it can be hard to figure out which change caused the problem.

The Need for Better Version Control

Traditional version control systems like Git are great for code, but they don’t work well for:

  • Large datasets
  • Model weights
  • Training artifacts
  • Performance metrics
  • Deployment configurations

We need specialized tools that can:

  1. Track changes to all components of ML systems
  2. Link changes to performance metrics
  3. Make it easy to roll back to previous versions
  4. Provide clear audit trails

Proposed Solutions

1. Comprehensive Version Control

  • Track code, data, and model versions together
  • Use specialized tools for large files
  • Maintain links between components

2. Automated Change Tracking

  • Log all changes automatically
  • Track environment configurations
  • Record all training runs

3. Performance Monitoring

  • Monitor model performance continuously
  • Track resource usage and costs
  • Alert on significant changes

4. Documentation and Collaboration

  • Enforce documentation requirements
  • Make changes visible to team members
  • Enable easy collaboration

Best Practices

  1. Use Specialized Tools

    • DVC for data version control
    • MLflow for experiment tracking
    • Git for code version control

  2. Automate Everything

    • Automated testing
    • Continuous integration
    • Deployment pipelines

  3. Document Changes

    • Clear change descriptions
    • Performance impact analysis
    • Rollback procedures

  4. Monitor and Alert

    • Set up monitoring dashboards
    • Define alert thresholds
    • Create incident response plans

Conclusion

Effective change tracking is crucial for maintaining and improving ML systems. By implementing proper version control and monitoring systems, teams can:

  • Reduce debugging time
  • Improve model reliability
  • Enable faster iterations
  • Maintain compliance requirements

In the next part of this series, we’ll discuss what data to collect for effective ML operations.