CI/CD Pipeline for Machine Learning Projects¶

Introduction¶

CI/CD for ML includes normal software checks plus data validation, training, evaluation, artifact packaging, container builds, and deployment gates.

Why This Matters¶

ML teams need automation because manual promotion hides mistakes. A model should pass checks and leave an audit trail before production.

Core Concepts¶

CI/CD should cover unit tests, data validation, training smoke tests, evaluation thresholds, model artifacts, container build, staging deployment, and smoke tests.

Practical Example¶

GitHub Actions-style workflow:

name: mlops-ci
on: [push]
jobs:
  validate-train-package:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
    - run: pip install -r requirements.txt
    - run: pytest
    - run: python pipelines/validate_data.py --input data/sample.csv
    - run: python pipelines/train.py --data data/sample.csv --out models/model.pkl
    - run: python pipelines/evaluate.py --model models/model.pkl --min-f1 0.80
    - run: docker build -t registry.example.com/ml-api:${{ github.sha }} .

How This Fits in a Production Workflow¶

The pipeline should publish artifacts only after evaluation succeeds. Deployment should use immutable image tags and model versions.

Common Mistakes¶

• Running tests but skipping model evaluation.
• Building containers with untracked model files.
• Deploying latest.
• Not saving evaluation reports as artifacts.

Quick Checklist¶

• Does CI validate data?
• Are metrics quality-gated?
• Is the model artifact stored?
• Is the container image immutable?
• Is staging tested before production?

Related Guides¶

• Model Evaluation in MLOps
• Docker for MLOps
• MLOps Production Checklist

Summary¶

Learn how CI/CD for machine learning validates code, data, models, containers, and deployments.