CI/CD for ML Pipelines

Summary

Automating testing, validation and deployment for ML code and models — extends standard CI/CD with ML-specific checks like data validation and model performance gates.

How to Learn This

1Set up a CI pipeline that runs tests and a basic model-performance check on every push.
2Learn what should gate a deployment: code tests plus a minimum accuracy threshold.
3Automate retraining or redeployment triggers where it makes sense for your project.

Experiment Tracking (MLflow, Weights & Biases)

Model Monitoring & Drift Detection

More topics in MLOps & Model Deployment

Model Serving (FastAPI/Flask)Docker for ML Models Model Versioning Experiment Tracking (MLflow, Weights & Biases)Model Monitoring & Drift Detection A/B Testing Models Batch vs Real-time Inference Kubernetes for ML (Overview)Feature Stores (Overview)

Stuck on this topic? Ask an Insider

Get 1:1 guidance from people who've walked this exact path — free on the InsideEdge app.

Download