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# Classical MLOps With UV

Tutorial site: <https://thomasbury.github.io/mlops-uv/>

[![Tests](https://github.com/ThomasBury/mlops-uv/actions/workflows/main.yaml/badge.svg)](https://github.com/ThomasBury/mlops-uv/actions/workflows/main.yaml)
[![Documentation](https://github.com/ThomasBury/mlops-uv/actions/workflows/docs.yaml/badge.svg)](https://github.com/ThomasBury/mlops-uv/actions/workflows/docs.yaml)
[![Python 3.10-3.12](https://img.shields.io/badge/python-3.10--3.12-blue.svg)](https://www.python.org/downloads/)
[![License: Apache-2.0](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://raw.githubusercontent.com/ThomasBury/mlops-uv/main/LICENSE)

`acebet` is a compact MLOps tutorial for a tabular tennis match predictor. The
repo covers prepared-data validation, feature assembly, model training, FastAPI
serving, tests, Docker packaging, and optional MLflow tracking without adding
infrastructure that the code does not actually implement.

The canonical documentation source lives in `docs/` and is built by Zensical
using `zensical.toml`. For the full walkthrough, start there.

## Quickstart

```bash
uv sync
just data-check
just feature-state
just train
just test
just serve
```

Then open `http://127.0.0.1:8000/docs` for OpenAPI.

## Docs Workflow

```bash
just docs-serve
just docs-build
```

Use those commands to preview or build the Zensical site from `docs/`.

## Read More

- `docs/getting-started.md` for local setup and first run
- `docs/classical-mlops-tutorial.md` for the end-to-end workflow
- `docs/uv/uv-workflows-for-mlops.md` for repo command conventions
- `docs/user-guide/training-and-inference.md` for artifacts and feature-contract details
- `docs/api/sync-endpoints.md` for the current API surface

## GitHub Star History

[![Star History Chart](https://api.star-history.com/svg?repos=ThomasBury/mlops-uv&type=Date)](https://star-history.com/#ThomasBury/mlops-uv&Date)

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