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README.md
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README.md
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#  GreatAI
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> GreatAI helps you easily transform your prototype AI code into production-ready software.
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[](https://sonar.scoutinscience.com/dashboard?id=great-ai)
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[](https://sonar.scoutinscience.com/dashboard?id=great-ai)
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[](https://sonar.scoutinscience.com/dashboard?id=great-ai)
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[](https://github.com/schmelczer/great-ai/actions/workflows/test.yml)
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[](https://github.com/schmelczer/great-ai/actions/workflows/publish.yaml)
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[](https://github.com/schmelczer/great-ai/actions/workflows/docker.yaml)
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[](https://badge.fury.io/py/great-ai)
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[](https://pepy.tech/project/great-ai)
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[Check out the documentation here](https://great-ai.scoutinscience.com/).
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## Find `great-ai` on [DockerHub](https://hub.docker.com/repository/docker/schmelczera/great-ai)
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```sh
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docker run -p6060:6060 schmelczera/great-ai
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```
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Find the dashboard at [http://localhost:6060](http://localhost:6060/dashboard/).
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## Find `great-ai` on [PyPI](https://pypi.org/project/great-ai/)
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GreatAI helps you easily transform your prototype AI code into production-ready software.
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[Check out the full documentation here](https://great-ai.scoutinscience.com).
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```sh
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pip install great-ai
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```
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> Create a new file called `main.py`
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```python
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from great_ai import GreatAI
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@ -35,19 +23,46 @@ from great_ai import GreatAI
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def hello_world(name: str) -> str:
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return f"Hello {name}!"
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```
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> Create a new file called `main.py`
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Deploy by executing `great-ai main.py`
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> Or: `great_ai main.py`
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Start it by executing `great-ai main.py`, find the dashboard at [http://localhost:6060](http://localhost:6060/dashboard).
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> Or: `python3 -m great_ai main.py`
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Find the dashboard at [http://localhost:6060](http://localhost:6060/dashboard/).
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That's it. Your GreatAI service is ready for production use. Many of the [SE4ML best-practices](https://se-ml.github.io) are configured and implemented automatically (of course, these can be customised as well).
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### Contribute
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## Why is this GREAT?
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#### Install
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GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, a number of best practices can be automatically implemented aiming to achieve the following attributes:
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- **G**eneral: use any Python library without restriction
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- **R**obust: have error-handling and well-tested utilities out-of-the-box
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- **E**nd-to-end: utilise end-to-end feedback as a built-in, first-class concept
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- **A**utomated: focus only on what actually requires your attention
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- **T**rustworthy: deploy models that you and society can confidently trust
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## Why GreatAI?
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There are other, existing solutions aiming to facilitate this phase. [Amazon SageMaker](https://aws.amazon.com/sagemaker) and [Seldon Core](https://www.seldon.io/solutions/open-source-projects/core) provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great.
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However, [research indicates](https://great-ai.scoutinscience.com) that professionals rarely use them. This may be due to their inherent setup and operating complexity. **GreatAI is designed to be as simple to use as possible.** Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the [SE4ML best-practices](https://se-ml.github.io), and thus, can meaningfully improve your deployment without requiring prohibitively large effort.
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## Find `great-ai` on [DockerHub](https://hub.docker.com/repository/docker/schmelczera/great-ai)
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```sh
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docker run -p6060:6060 schmelczera/great-ai
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```
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## Learn more
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[Check out the documentation](https://great-ai.scoutinscience.com).
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## Contribute
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Contributions are welcome.
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### Install for development
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```sh
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python3 -m venv --copies .env
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@ -56,8 +71,8 @@ python3 -m pip install flit
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python3 -m flit install --symlink --deps=all
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```
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#### Documentation
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### Serve documentation
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```sh
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mkdocs serve --dirtyreload
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```
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```
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@ -4,12 +4,12 @@
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</div>
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[](https://sonar.scoutinscience.com/dashboard?id=great-ai)
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[](https://sonar.scoutinscience.com/dashboard?id=great-ai)
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[](https://sonar.scoutinscience.com/dashboard?id=great-ai)
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[](https://github.com/schmelczer/great-ai/actions/workflows/test.yml)
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[](https://github.com/schmelczer/great-ai/actions/workflows/publish.yaml)
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[](https://github.com/schmelczer/great-ai/actions/workflows/docker.yaml)
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[](https://badge.fury.io/py/great-ai)
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[](https://pepy.tech/project/great-ai)
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Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing [unintended biases](https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction){ target=_blank }. GreatAI helps fixing this by allowing you to ==easily transform your prototype AI code into production-ready software==.
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@ -42,6 +42,7 @@ Applying AI is becoming increasingly easier but many case studies have shown tha
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- [x] Deployable Jupyter Notebooks
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- [x] Dashboard for high-level overview and analysing traces
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- [ ] Support for direct file input
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- [ ] Support for PostgreSQL
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## Hello world
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@ -85,7 +86,7 @@ great-ai hello-world.py
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GreatAI fits between the prototype and deployment phase of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, a number of best practices can be automatically implemented aiming to achieve the following attributes:
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GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, a number of best practices can be automatically implemented aiming to achieve the following attributes:
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- **G**eneral: use any Python library without restriction
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- **R**obust: have error-handling and well-tested utilities out-of-the-box
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