Key Takeaway
The key difference between CI/CD for software and ML is that ML pipelines must treat data changes as first-class triggers alongside code changes, and evaluation gates must prevent both accuracy regressions and fairness degradations. This guide covers a six-stage pipeline architecture with pipeline-as-code examples, evaluation gate design, and canary deployment strategies.
Prerequisites
- Existing CI/CD infrastructure for application code (GitHub Actions, GitLab CI, Jenkins, etc.)
- ML training code in version control with reproducible training scripts
- An evaluation dataset with ground truth labels for automated benchmarking
- A model registry for storing and versioning model artifacts (MLflow, SageMaker, etc.)
- Container infrastructure for reproducible training environments (Docker, Kubernetes)
ML Pipelines vs. Software Pipelines
Software CI/CD pipelines are triggered by code changes, run tests, and deploy application artifacts. ML CI/CD pipelines must handle two additional dimensions: data changes (a new training dataset should trigger retraining just as a code change triggers a rebuild) and model evaluation (passing unit tests is not sufficient -- the model must demonstrate that it meets quality thresholds on representative evaluation data). These additions make ML pipelines longer, more computationally expensive, and more complex to debug than software pipelines.
The pipeline architecture described here is designed for teams that already have software CI/CD in place. It extends your existing pipeline with ML-specific stages rather than replacing it. The model training, evaluation, and deployment stages are implemented as additional workflow steps that run after traditional code quality checks pass.
The Six Pipeline Stages
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