MLOps Practices

MLOps Fundamentals

Operationalizing ML models.

Versioning

Model versioning: track versions. Data versioning: track data changes. Experiment tracking.

CI/CD for ML

Automated testing: data validation, model validation. Automated retraining triggers.

Monitoring

Model monitoring: performance degradation. Data monitoring: distribution shift. Business metrics.

Key Takeaways

1. MLOps applies DevOps to ML
2. Versioning and CI/CD essential
3. Monitoring prevents silent failures