What is CI/CD for ML?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

ML CI/CD adds three additional concerns: data validation to ensure training data quality before model building, model validation to verify accuracy, fairness, and performance thresholds, and model-specific deployment strategies like canary and shadow deployments. Traditional CI runs unit and integration tests. ML CI adds data quality checks, model training, and evaluation against acceptance criteria. ML CD adds model-specific deployment gates and monitoring. The pipeline is longer but each step prevents a different category of production failure.

Test data preprocessing code with unit tests on known inputs and expected outputs. Validate training data quality with completeness and distribution checks. Run model training on a small data subset to verify the training code works. Evaluate the trained model against holdout data and acceptance criteria. Check model artifact packaging and deployment configuration. Validate model serving endpoint health after deployment. Each step catches a different failure type and should block the pipeline on critical failures.

Target 30-60 minutes for the full pipeline excluding model training. Data validation and unit tests should complete in 5-10 minutes. Quick model training on a subset should take 10-20 minutes to verify the code works. Model evaluation on holdout data takes 5-15 minutes. Deployment and health checks take 5-10 minutes. Full model training can run as a separate triggered pipeline that takes hours but doesn't block rapid iteration on code changes. Optimize for fast feedback on code changes while thorough validation gates protect production.

ML CI/CD adds three additional concerns: data validation to ensure training data quality before model building, model validation to verify accuracy, fairness, and performance thresholds, and model-specific deployment strategies like canary and shadow deployments. Traditional CI runs unit and integration tests. ML CI adds data quality checks, model training, and evaluation against acceptance criteria. ML CD adds model-specific deployment gates and monitoring. The pipeline is longer but each step prevents a different category of production failure.

Test data preprocessing code with unit tests on known inputs and expected outputs. Validate training data quality with completeness and distribution checks. Run model training on a small data subset to verify the training code works. Evaluate the trained model against holdout data and acceptance criteria. Check model artifact packaging and deployment configuration. Validate model serving endpoint health after deployment. Each step catches a different failure type and should block the pipeline on critical failures.

Target 30-60 minutes for the full pipeline excluding model training. Data validation and unit tests should complete in 5-10 minutes. Quick model training on a subset should take 10-20 minutes to verify the code works. Model evaluation on holdout data takes 5-15 minutes. Deployment and health checks take 5-10 minutes. Full model training can run as a separate triggered pipeline that takes hours but doesn't block rapid iteration on code changes. Optimize for fast feedback on code changes while thorough validation gates protect production.

ML CI/CD adds three additional concerns: data validation to ensure training data quality before model building, model validation to verify accuracy, fairness, and performance thresholds, and model-specific deployment strategies like canary and shadow deployments. Traditional CI runs unit and integration tests. ML CI adds data quality checks, model training, and evaluation against acceptance criteria. ML CD adds model-specific deployment gates and monitoring. The pipeline is longer but each step prevents a different category of production failure.

Test data preprocessing code with unit tests on known inputs and expected outputs. Validate training data quality with completeness and distribution checks. Run model training on a small data subset to verify the training code works. Evaluate the trained model against holdout data and acceptance criteria. Check model artifact packaging and deployment configuration. Validate model serving endpoint health after deployment. Each step catches a different failure type and should block the pipeline on critical failures.

Target 30-60 minutes for the full pipeline excluding model training. Data validation and unit tests should complete in 5-10 minutes. Quick model training on a subset should take 10-20 minutes to verify the code works. Model evaluation on holdout data takes 5-15 minutes. Deployment and health checks take 5-10 minutes. Full model training can run as a separate triggered pipeline that takes hours but doesn't block rapid iteration on code changes. Optimize for fast feedback on code changes while thorough validation gates protect production.