MLOps & Production ML
Training is the easy part. This track is everything after: versioning, serving, drift, and the on-call for a model.
Mastery unlocks after a 90% Quiz score
Real-world analogy
A model in a notebook is a prototype car on a stand. MLOps is the road, the fuel, the dashboard lights, and the mechanic on the night shift.
◎ Why it matters in production
The model that won the hackathon silently decayed three weeks after the data distribution shifted. That's the story this track exists to prevent.
Fundamental concepts you will master
Data is the artifact
Version the data, the code, and the model together — or you cannot reproduce a result.
Serving is a product
Latency budgets, batch vs real-time, and a rollback that isn't 'retrain'.
Drift
The world changed. Your F1 did not get the memo.
Human loop
Where a person still has to approve, label, or override.
Step-by-step curriculum
From notebook to pipeline
Reproducible training with pinned data and configs.
Registries & lineage
Which model is in prod, trained on what, by whom.
Serving patterns
Batch, online, streaming. Pick with the latency in mind.
Monitoring
Data drift, performance drift, and alerts a human will not ignore.
Incidents
Rollback, shadow deploy, and the postmortem a model deserves.
Ready to see it in action?
Build your own path, step through operations, and watch mastery unlock after 90%.
Pointer Phantom
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+1,250 XP → Cache Ghost