# Statistics & MLOps

Hypothesis testing, Bayesian thinking, FastAPI deployment, ML monitoring, and feature stores.

Use this subtrack when your weak point is the bridge from analysis to production discipline. It is especially useful if you can train models but still feel less confident about experiments, service design, or monitoring.

## How To Use This Subtrack Well

- Treat the statistics and operations pieces as one workflow: measure, ship, monitor, improve.
- Build one small deployable project with clear evaluation criteria.
- Pair this subtrack with [../../09-mlops/README.md](../../09-mlops/README.md) and [../../16-model-evaluation/README.md](../../16-model-evaluation/README.md) for the fuller picture.

## What Comes Next

- Continue to [../README.md](../README.md) for the broader practical data science roadmap.
- Continue to [../../09-mlops/README.md](../../09-mlops/README.md) if you want deeper deployment and monitoring coverage.
- Continue to [../../16-model-evaluation/README.md](../../16-model-evaluation/README.md) if your focus shifts more toward measurement than operations.
