End-to-End MLOps Pipeline
Build the full MLOps lifecycle: training, registry, serving, monitoring, retraining triggers.
MLflow or W&BBentoML or KServeKubernetesPrometheus
About this project
MLOps is the production-engineering side of ML. This project teaches the full lifecycle: experiment tracking (W&B or MLflow), model registry, deployment (canary, A/B), monitoring (drift, latency, accuracy), and automated retraining. Build it for any model (churn, classifier, regression) — the pipeline is the point, not the model.
Why build this in 2026?
MLOps is the most-asked-about specialty in ML eng interviews in 2026. Few candidates have shipped it.
What you'll ship
- GitHub repo
Architecture diagram
Demo: model deployed, monitored, retrained
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Skills you'll practice
pythonmlopskubernetesdocker