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Automate testing, building, and deploying ML systems with modern CI/CD tools and practices
Continuous Integration and Continuous Deployment (CI/CD) is the practice of automatically building, testing, and deploying software every time code changes. For ML systems, CI/CD extends beyond traditional software to encompass data validation, model quality gates, and artifact management --- ensuring that not just code, but also data and models, meet quality standards before reaching production.
A well-designed CI/CD pipeline catches bugs in minutes rather than days, enforces code quality standards consistently, and makes deployments boring and repeatable rather than stressful and error-prone. For ML teams, this means every model that reaches production has been automatically tested against accuracy thresholds, latency SLAs, regression test suites, and data quality checks.
This chapter covers the complete CI/CD stack: from Git workflows and GitHub Actions basics, through testing and Docker image building, to deployment strategies like blue-green and canary releases. We then extend into ML-specific concerns: data versioning with DVC, model validation gates, and artifact lifecycle management with MLflow. By the end, you will be able to design and implement a production-grade pipeline that takes an ML model from a developer's laptop to serving real traffic with confidence.
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Branching strategies, pull requests, code review — the collaboration substrate every CI/CD pipeline builds on.
Workflows trigger on push, tests gate the merge
YAML workflows, jobs, steps, runners, triggers — turning every git push into an automated build.
Unit tests, pytest, ruff, type checks — the guardrails that catch regressions before they reach main.
Package the artifact, then promote it through environments
Docker images, wheel files, build caches, multi-arch builds — producing reproducible artifacts from source.
Staging, blue-green, canary, rollback — promoting code from main to production safely.
Credentials stay out of code, ML pipelines validate data and models
GitHub Secrets, OIDC, environment variables, vaults — keeping credentials out of code and logs.
Data validation, model training, evaluation gates, model registry — CI/CD adapted for non-deterministic ML artifacts.
Container registries, model registries, versioning, retention policies — the storage layer for everything CI/CD produces.
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