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MLOps overview

**MLOps** is the practice of treating ML like a production system: data, training, deployment, monitoring, and rollback—not a one-off notebook. Google Cloud’s architecture guide defines MLOps as unifying ML *development* and *operations*...

What it is

MLOps is the practice of treating ML like a production system: data, training, deployment, monitoring, and rollback—not a one-off notebook. Google Cloud’s architecture guide defines MLOps as unifying ML *development* and *operations* with automation and monitoring at every step (integration, testing, release, deployment, infrastructure).

<!-- IMAGE: notebook → pipeline → serve → monitor loop -->

HIGH PRIORITYFLOWCHART
◷ IN PRODUCTION

Visual Spec & Architecture Diagram

MLOps loop: Data → Train → Validate → Deploy → Monitor → Retrain, with Model Registry and Feature Store as side hubs. Title: 'MLOps lifecycle'.

Educational Focus: Course north-star diagram.

Why it matters

A strong offline metric is not a production system. Google’s framing (adapting Sculley et al.) shows ML *code* is a small box surrounded by configuration, data collection/verification, testing, serving, and monitoring. Most failures live in that surrounding system.

How it works (plain)

  1. Version data, code, and configs together.
  2. Train in a reproducible pipeline (not only an interactive notebook).
  3. Validate data and model before promote.
  4. Deploy with a rollback path.
  5. Monitor quality, latency, cost, and safety signals.
  6. Retrain or roll back when live behavior drifts.

LLM apps add prompt/versioning, tool-permission reviews, and eval gates (Course 07/08/10).

Everyday example

A restaurant that scales a recipe needs suppliers, checklists, and health inspections—not only a tasty first plate. MLOps is the kitchen ops for models.

Try it

Pick one AI feature at work. Write the top 3 things you would monitor in the first 24 hours after a change (quality, latency, cost, or safety).

Myths

⚠️ Myth: Test-set accuracy equals production readiness.
✓ Reality: Drift, skew, latency, and abuse appear only live.
⚠️ Myth: MLOps is only for huge teams.
✓ Reality: Solo builders still need versioning, a registry habit, and basic monitoring.
⚠️ Myth: MLOps = “deploy the model API.”
✓ Reality: Mature setups deploy *pipelines* that can retrain and re-serve (Google Cloud levels 0→2).

Sources