GitHub Actions ML Pipeline: First CI/CD Portfolio Project

#mlops #cicd #githubactions #mlflow

The Pipeline That Got Me Interviews

Most ML portfolios show trained models. Few show automated training pipelines that actually run on every commit.

That's the gap. A GitHub Actions ML pipeline isn't just DevOps theater — it's proof you understand the full lifecycle. When a recruiter sees "CI/CD" next to "PyTorch," they know you've deployed code that had to work without you babysitting it.

I'll show you a working pipeline that trains a scikit-learn model, tracks experiments with MLflow, and deploys to GitHub Pages. The entire setup takes about 90 minutes, and you can adapt it to any sklearn-compatible model. By the end, you'll have a badge on your README showing green builds, and a public dashboard showing training metrics over time.