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https://github.com/online-ml/beaver

đŸĻĢ MLOps for (online) machine learning
https://github.com/online-ml/beaver

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đŸĻĢ MLOps for (online) machine learning

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đŸĻĢ Beaver â€ĸ MLOps for (online) machine learning


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Beaver is...

✔ [*The whole package*](https://www.youtube.com/watch?v=nzFTmJnIakk&list=PLIU25-FciwNaz5PqWPiHmPCMOFYoEsJ8c&index=5) â€ĸ it's a framework to develop, deploy, and maintain machine learning models. And that includes feature engineering.

✔ *No fuss* â€ĸ there's an SDK to do stuff, and a UI to see stuff.

✔ *Online-first* â€ĸ it is designed for online machine learning models, while also supporting batch models.

✔ *Opinionated* â€ĸ it encourages you to [process data with SQL](https://www.ethanrosenthal.com/2022/05/10/database-bundling/) and define models in Python.

✔ [*Interfaces all the way down*](https://vadosware.io/post/building-an-interface-with-one-implementation-is-unquestionably-right/) â€ĸ you can plug in your existing message broker, stream processor, model store, etc. This is called a [hexagonal architecture](https://www.wikiwand.com/en/Hexagonal_architecture_(software)) in software design.

✔ *Batteries included* â€ĸ default infrastructure and monitoring are provided if needed.

## 🤱 Getting started

There is a [pre-built image](https://ghcr.io/online-ml/beaver) you can pull and run đŸŗ

```sh
docker pull ghcr.io/online-ml/beaver:latest
docker run ghcr.io/online-ml/beaver
```

You can also build the provided [`docker-compose.yaml`](docker-compose.yaml) yourself:

```sh
git clone [email protected]:online-ml/beaver.git
cd beaver
docker-compose up
```

Go to [http://localhost:8501](http://localhost:8501/) to access the user interface.

This is all you need to run Beaver. Check out the examples to see how to use it.

## 👀 Examples

- [🚕 Taxis](examples/taxis)
- [đŸŒŗ Batch gradient boosting vs. random forest](examples/batch-trees)

## 📝 License

Beaver is free and open-source software licensed under the [3-clause BSD license](LICENSE).