CI/CD for ML
Definition
CI/CD for ML extends continuous integration and deployment practices to machine learning, automating testing, validation, and deployment of both code and model artifacts throughout the ML lifecycle.
Why It Matters
Traditional CI/CD catches bugs before they reach production. ML CI/CD does the same, but “bugs” include data issues, model degradation, and performance regressions, which are problems that don’t show up in code reviews.
Without ML CI/CD, teams deploy models manually and hope for the best. Changes introduce regressions nobody notices until production metrics suffer. Rollbacks are painful. Data scientists are afraid to experiment because deployment is a multi-day ordeal.
For AI engineers, robust CI/CD is what separates prototype code from production systems. It enables rapid iteration: try new approaches, validate automatically, deploy safely, monitor results, repeat.
Implementation Basics
ML CI/CD has components beyond traditional software:
1. Code CI (Traditional) Unit tests for feature engineering and data processing. Integration tests for pipeline components. Code linting and type checking. Run on every commit.
2. Data Validation Schema validation (are columns present, correct types?). Statistical tests (are distributions within expected ranges?). Data quality checks (null rates, cardinality). Block pipelines when data issues detected.
3. Model Validation Performance thresholds (accuracy must be > X%). Regression tests against holdout sets. Comparison against current production model. Bias and fairness checks. Latency and memory requirements.
4. Integration Testing End-to-end pipeline tests with realistic data samples. API contract testing for model endpoints. Load testing for serving infrastructure.
5. Continuous Deployment Automated model registration on successful validation. Canary deployments (small traffic percentage first). Automated rollback on metric degradation. Blue-green deployments for zero-downtime updates.
6. Continuous Training Scheduled or triggered retraining on new data. Automatic validation of new model versions. Champion/challenger comparisons before promotion.
Implementation Tools:
- GitHub Actions, GitLab CI, Jenkins for orchestration
- Great Expectations, Pandera for data validation
- MLflow, DVC for artifact tracking
- Kubernetes, Argo CD for deployment
- Custom scripts for model validation
Start with data validation, as bad data causes most ML failures. Add model validation when you have metrics to track. Automate deployment once validation is reliable.
Source
CI/CD for ML involves continuous integration of source code, continuous delivery of ML pipelines, and continuous training to automatically retrain models in production.
https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning