Complete GitHub Actions Tutorial for ML CI/CD

GitHub Actions enables you to automate Machine Learning workflows from testing to deployment. In this tutorial, we'll build a comprehensive CI/CD pipeline for ML projects, including data validation, model training, testing, and deployment.

Why CI/CD for ML?

Challenges in ML projects:

• Reproducibility: Ensuring consistent results across environments
• Testing: Validating data, models, and code
• Automation: Reducing manual work and human error
• Collaboration: Standardizing workflows across teams
• Monitoring: Tracking performance and detecting regressions

• Automated testing on every push/PR
• Scheduled retraining
• Model validation gates
• Automated deployment
• Integration with cloud services

GitHub Actions Basics

1. Workflow File Structure

# .github/workflows/ml-pipeline.yml
name: ML Pipeline

Triggers
on:
  push:
    branches: [main, develop]
  pullrequest:

    branches: [main]
  schedule:
cron: '0 0   0'  # Weekly on Sunday
  workflowdispatch:  # Manual trigger


Environment variables
env:
  PYTHONVERSION: '3.10'

  MODELNAME: 'my-model'

Jobs
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
uses: actions/checkout@v4
name: Setup Python
        uses: actions/setup-python@v5

        with:
          python-version: ${{ env.PYTHONVERSION }}

name: Install dependencies
        run: pip install -r requirements.txt

name: Run tests
        run: pytest tests/

on:
  # Push to specific branches
  push:
    branches: [main]
    paths:
'src/'

'tests/'
'requirements.txt'

  # Pull requests
  pullrequest:
    branches: [main]

  # Scheduled runs
  schedule:
cron: '0 2   '  # Daily at 2 AM UTC


  # Manual trigger with inputs
  workflowdispatch:

    inputs:
      environment:
        description: 'Deployment environment'
        required: true
        default: 'staging'
        type: choice
        options:
staging
production
      retrain:

        description: 'Force retrain model'
        required: false
        type: boolean
        default: false

name: Code Quality & Tests

on:
  push:
    branches: [main, develop]
  pullrequest:
    branches: [main]

jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
uses: actions/checkout@v4
name: Setup Python
        uses: actions/setup-python@v5

        with:
          python-version: '3.10'
name: Install linters
        run: |

          pip install flake8 black isort mypy
name: Run flake8
        run: flake8 src/ tests/

name: Check black formatting
        run: black --check src/ tests/

name: Check import sorting
        run: isort --check-only src/ tests/

name: Run mypy
        run: mypy src/


  test:
    runs-on: ubuntu-latest
    needs: lint
    steps:
uses: actions/checkout@v4
name: Setup Python
        uses: actions/setup-python@v5

        with:
          python-version: '3.10'
name: Cache pip
        uses: actions/cache@v4

        with:
          path: ~/.cache/pip
          key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
name: Install dependencies
        run: pip install -r requirements.txt

name: Run unit tests
        run: pytest tests/unit/ -v --cov=src --cov-report=xml

name: Upload coverage
        uses: codecov/codecov-action@v4

        with:
          files: ./coverage.xml