# Introduction

> Lesson 01 · MLOps

## What is MLOps?

MLOps applies engineering and operations practices to the machine learning lifecycle so models
can be built, shipped, and maintained reliably and repeatably.

## Why it matters

A model that works in a notebook is not a product. Production adds data drift, reproducibility,
deployment, scaling, and monitoring concerns that the modelling step alone does not address.

## The ML lifecycle

1. Data collection and versioning.
2. Experimentation and tracking.
3. Training pipelines and automation.
4. Deployment.
5. Monitoring and feedback.

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Next: The ML Lifecycle *(planned)* · [Course overview](/en/MLOps)