TL;DRMLOps brings engineering discipline to ML: pipelines, deployment, monitoring, and drift detection.
A model in a notebook isn't a product. MLOps covers the lifecycle: reproducible data/training pipelines, versioning of data/models, deployment, and monitoring in production — including drift (when live data diverges from training data, quietly degrading accuracy). It's DevOps for ML, where the model and the data are first-class artifacts that must be tracked and re-validated over time.
Key points
Lifecycle: pipelines → deploy → monitor
Version data and models, not just code
Watch for data/concept drift
DevOps discipline for ML
Common mistakes
Shipping a notebook model with no pipeline
No monitoring → silent accuracy decay
Not versioning data/models
Try it: Explain what data drift is and why it silently breaks models.
Example code
<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>train on 2024 data → deploy
2026 live data differs (drift) → accuracy quietly drops
MLOps: monitor + retrain</pre></body></html>