CI/CD for ML
Continuous integration and continuous delivery adapted for machine learning workflows with data, code, and model validation.
CI/CD for ML automates testing, validating, and deploying ML models – beyond code, also for data quality and model performance.
Explanation
ML CI/CD extends classic CI/CD with data validation, model training pipelines, performance regression tests, model registry integration, and automated canary deployment.
Marketing Relevance
CI/CD for ML is essential for reproducible, reliable ML systems in production.
Common Pitfalls
Only testing code, not data and models. Not defining performance baselines. Training in CI too slow.
Origin & History
Google published the influential MLOps whitepaper with three maturity levels for ML CI/CD in 2020. GitHub Actions and GitLab CI/CD were adapted for ML workflows. Tools like CML (DVC) made ML CI/CD more accessible from 2020.
Comparisons & Differences
CI/CD for ML vs. Traditional CI/CD
Traditional CI/CD tests code; ML CI/CD additionally tests data quality, model performance, and training reproducibility.
CI/CD for ML vs. MLOps
CI/CD is a building block of MLOps; MLOps additionally covers monitoring, governance, and the entire ML lifecycle.