data pipelines

A practical guide to AI‑DevOps projects: build end‑to‑end MLOps pipelines, version data and features, containerize training/inference, automate CI/CD, use IaC and orchestration for scalable deployment, and add monitoring for drift, bias, and performance with alerts/rollbacks. Offers tool choices and step‑by‑step project ideas from prototype to production.