Introduction

Lesson 01 · MLOps

Objectives

• Understand what MLOps is and why models need more than good accuracy.
• Recognize how ML systems differ from traditional software.
• See the lifecycle the rest of the course builds on.

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.

Next: The ML Lifecycle (planned) · Course overview