# MLOps

> Deploy, monitor, and maintain ML models as production systems — experiment tracking, serving, CI/CD, and cloud deployment.

![Status: complete](https://img.shields.io/badge/status-complete-brightgreen)

Open [`09-mlops.ipynb`](./09-mlops.ipynb) for learning objectives, prerequisites, and recommended reading order.

## Topics Covered

- MLflow experiment tracking and model registry
- FastAPI model serving and Docker containerisation
- Data drift detection and production monitoring
- GitHub Actions CI/CD pipelines for ML
- Cloud deployment (AWS, GCP, Azure)

## Prerequisites

[08-rag/README.md](../08-rag/README.md)

## What Comes Next

[10-specializations/README.md](../10-specializations/README.md) or [11-prompt-engineering/README.md](../11-prompt-engineering/README.md)
