From zero to MLOps: GitHub Actions for ML pipelines

 · 8 min read

CI/CD isn’t just for web apps. ML pipelines benefit enormously from the same automation — reproducible training runs, automated evaluation, and safe model promotion.

Setup

Add .github/workflows/ml-pipeline.yml to your repo. Three jobs: test, train, evaluate.

name: ML Pipeline
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with: { python-version: "3.11" }
      - run: pip install -r requirements.txt && pytest tests/
  train:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: python train.py --output-dir ./model-artifacts
      - uses: actions/upload-artifact@v4
        with:
          name: model
          path: ./model-artifacts

That handles test → train. Add an evaluate step to gate on metric thresholds before promoting.

Applying this to ML pipelines

Unlike standard CI, ML pipelines need to cache datasets and model weights to avoid re-downloading on every run. Use actions/cache with a hash of your data/ folder.

- uses: actions/cache@v4
  with:
    path: ~/.cache/huggingface
    key: hf-${{ hashFiles('requirements.txt') }}

This alone can cut pipeline time from 20 minutes to 3 minutes on repeated runs.

Common Errors & Fixes

  • pytest fails on import: add src/ to PYTHONPATH in the workflow env block.
  • Training OOM: use runs-on: ubuntu-latest-4-cores or self-hosted GPU runners for heavy models.
  • Artifact too large: save only the final weights, not the full checkpoint directory.

Checklist

  • Workflow file at .github/workflows/ml-pipeline.yml
  • Secrets for model registry configured (e.g., MLFLOW_TRACKING_URI)
  • Evaluation step gates deployment on minimum accuracy threshold
  • First push triggers green check