Data Quality in ML Pipelines

Short reference on data quality in production ML systems.

Key Concepts

• Version control for models
• Staging and production promotion
• Monitoring and alerts
• Data quality assurance

Best Practices

1. Always version your models
2. Test before promotion
3. Monitor in production
4. Have rollback plans

Tools and Solutions

• MLflow Model Registry
• HuggingFace Hub
• Custom solutions

Sources and Links

• MLflow Model Registry
• HuggingFace Hub