CI/CD for ML: GitHub Actions and DVC Pipelines

Software teams long ago automated the path from a code change to a tested, deployed release through continuous integration and continuous delivery, but machine learning breaks the assumptions CI/CD was built on, because an ML system changes not only when code changes but when data or the model changes, and its tests must validate statistical behaviour, not just function outputs. CI/CD for ML extends the automation to this reality: every change to code, data, or pipeline triggers an automated run that reproduces the pipeline, validates data and model quality, and gates promotion. GitHub Actions provides the automation engine, running workflows on every push or pull request, while DVC pipelines provide the reproducible, incrementally-recomputable ML stages that those workflows execute. The problem this solves is the manual, error-prone, unrepeatable way ML changes otherwise reach production, someone runs a notebook, eyeballs a metric, and copies a file. CI/CD for ML replaces that with a pipeline where a pull request can retrain, evaluate against the current model, post a metrics diff for review, and block a merge that would regress quality, making model changes as reviewable and safe as code changes.