# The MLOps Lifecycle — MLOps

Source: https://www.skillbyai.com/en/mlops/i-cycle

> A loop, not a hand-off.

## Data, train, deploy, monitor, repeat

The lifecycle is a loop: **data** collection and validation; **experimentation** with tracked runs; **training pipelines** that are automated and reproducible; **evaluation** against the current model with gates; **registration** of approved model versions; **deployment** via batch jobs or services with safe rollout; **monitoring** of systems, data and model quality; and **retraining** when monitoring or a schedule says so. Each arrow is a hand-off that automation and versioning make reliable.

## A restaurant kitchen, not a cooking competition

A competition rewards one perfect dish; a restaurant must serve the same quality every night with changing ingredients, staff and orders. MLOps is the kitchen system.

## Automate the slowest hand-off first

If deploying takes weeks of manual steps, automate deployment before tuning models further.

**Quiz:** What closes the MLOps loop after deployment?

- [x] Monitoring that triggers retraining or rollback
- [ ] Deleting the training data
- [ ] Writing a slide deck
- [ ] Turning off logging

*Answer:* Monitoring that triggers retraining or rollback. Monitoring feeds the next iteration.