andras/great-ai
1
0
Fork 0
Code Issues Pull requests 1 Projects Releases 4 Packages Wiki Activity Actions

Deployed 63a4598 with MkDocs version: 1.3.0

Browse source
This commit is contained in:
2022-07-09 21:12:02 +00:00
parent a388bcf449
commit 38c201a13f
51 changed files with 7876 additions and 1447 deletions
Download patch file Download diff file Expand all files Collapse all files

45
index.md
View file

@ -1,29 +1,30 @@
# Overview of GreatAI
<div style="display: flex; justify-content: space-between; align-items: center;">
<h1 style="margin: 0">Overview of GreatAI</h1>
<img src="media/logo.png" width=80>
</div>
[![Test](https://github.com/schmelczer/great-ai/actions/workflows/test.yml/badge.svg)](https://github.com/schmelczer/great-ai/actions/workflows/check.yml)
[![Quality Gate Status](https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&metric=alert_status)](https://sonar.schmelczer.com/dashboard?id=great-ai)
[![Publish on PyPI](https://github.com/schmelczer/great-ai/actions/workflows/publish.yaml/badge.svg)](https://github.com/schmelczer/great-ai/actions/workflows/publish.yaml)
[![Publish on DockerHub](https://github.com/schmelczer/great-ai/actions/workflows/docker.yaml/badge.svg)](https://github.com/schmelczer/great-ai/actions/workflows/docker.yaml)
[![Downloads](https://pepy.tech/badge/great-ai/month)](https://pepy.tech/project/great-ai)
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 }. To extend the list of available solutions, ==GreatAI helps you easily transform your prototype AI code into production-ready software.==
??? quote "Case studies"
"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature."
&mdash; [John et al.](https://ieeexplore.ieee.org/abstract/document/9359253)
&mdash; [John et al.](https://ieeexplore.ieee.org/abstract/document/9359253){ target=_blank }
"Finally, we have found that existing tools to aid Machine Learning development do not address the specificities of different projects, and thus, are seldom adopted by teams." &mdash; [Haakman et al.](https://link.springer.com/article/10.1007/s10664-021-09993-1)
"Because a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code, it may be less costly to create a clean native solution rather than re-use a generic package." &mdash; [Sculley et al.](https://www.researchgate.net/profile/Todd-Phillips/publication/319769912_Hidden_Technical_Debt_in_Machine_Learning_Systems/links/61e716d68d338833e37a7fd6/Hidden-Technical-Debt-in-Machine-Learning-Systems.pdf)
"For example, practice 25 is very important for “Traceability", yet relatively weakly adopted. We expect that the results from this type of analysis can, in the future, provide useful guidance for practitioners in terms of aiding them to assess their rate of adoption for each practice and to create roadmaps for improving their processes. &mdash; [Serban et al.](https://dl.acm.org/doi/abs/10.1145/3382494.3410681?casa_token=uCFz0dtDR6gAAAAA:4_8OMJ-5njwopYkB1KSGAu9JfbNq4nfa8LRE0fj84ckjfo-GgtcYQivZTGxal3M4haoA8r_xwpw)
"Finally, we have found that existing tools to aid Machine Learning development do not address the specificities of different projects, and thus, are seldom adopted by teams." &mdash; [Haakman et al.](https://link.springer.com/article/10.1007/s10664-021-09993-1){ target=_blank }
"Because a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code, it may be less costly to create a clean native solution rather than re-use a generic package." &mdash; [Sculley et al.](https://www.researchgate.net/profile/Todd-Phillips/publication/319769912_Hidden_Technical_Debt_in_Machine_Learning_Systems/links/61e716d68d338833e37a7fd6/Hidden-Technical-Debt-in-Machine-Learning-Systems.pdf){ target=_blank }
"For example, practice 25 is very important for “Traceability", yet relatively weakly adopted. We expect that the results from this type of analysis can, in the future, provide useful guidance for practitioners in terms of aiding them to assess their rate of adoption for each practice and to create roadmaps for improving their processes. &mdash; [Serban et al.](https://dl.acm.org/doi/abs/10.1145/3382494.3410681?casa_token=uCFz0dtDR6gAAAAA:4_8OMJ-5njwopYkB1KSGAu9JfbNq4nfa8LRE0fj84ckjfo-GgtcYQivZTGxal3M4haoA8r_xwpw){ target=_blank }
## Features
- [x] Save prediction traces of each prediction including arguments and model versions
- [x] Save feedback and merge it into a ground-truth database
- [x] Save feedback and merge it into a ground-truth database *:arrow_right: quasi-shadow deployment*
- [x] Version and store models and data on shared infrastructure *(MongoDB GridFS, S3-compatible storage, shared local-volume)*
- [x] Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration
- [x] Input validation
@ -33,11 +34,11 @@ Applying AI is becoming increasingly easier but many case studies have shown tha
- [x] Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing
- [x] Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.)
- [x] A simple, unified configuration interface
- [x] Fully-typed API for IntelliSense support
- [x] Fully-typed API for [Pylance](https://github.com/microsoft/pylance-release){ target=_blank } and [MyPy](http://mypy-lang.org){ target=_blank } support
- [x] Auto-reload for development
- [x] Deployable Jupyter Notebooks
- [x] Docker support for deployment
- [x] Dashboard for high-level overview and searching traces
- [ ] Shadow deployment
## Hello world
@ -66,7 +67,7 @@ def hello_world(name: str) -> str: #(2)
```sh title="terminal"
great-ai hello-world.py
```
> Navigate to [localhost:6060](http://127.0.0.1:6060/) in your browser.
> Navigate to [localhost:6060](http://127.0.0.1:6060) in your browser.
<div style="display: flex; justify-content: space-evenly;" markdown>
![](media/hello-world-dashboard.png){ loading=lazy }
@ -75,14 +76,13 @@ great-ai hello-world.py
</div>
!!! success
Your GreatAI service is ready for production use. Many of the [SE4ML best-practices](https://se-ml.github.io/){ target=_blank } are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.
Your GreatAI service is ready for production use. Many of the [SE4ML best-practices](https://se-ml.github.io){ target=_blank } are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.
## Why is this GREAT?
![scope of GreatAI](scope-simple.drawio.svg)
![scope of GreatAI](media/scope-simple.drawio.svg)
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 concerning the following 5 aspects:
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:
- **G**eneral: use any Python library without restriction
- **R**obust: have error-handling and well-tested utilities out-of-the-box
@ -92,13 +92,20 @@ GreatAI fits between the prototype and deployment phase of your (or your organis
## Why GreatAI?
There are other, existing solutions aiming to facilitate this phase. [Amazon SageMaker](https://aws.amazon.com/sagemaker/){ target=_blank } and [Seldon Core](https://www.seldon.io/solutions/open-source-projects/core){ target=_blank } provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great.
There are other, existing solutions aiming to facilitate this phase. [Amazon SageMaker](https://aws.amazon.com/sagemaker){ target=_blank } and [Seldon Core](https://www.seldon.io/solutions/open-source-projects/core){ target=_blank } provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great.
However, research indicates 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 [best-practices](https://se-ml.github.io/){ target=_blank }, and thus, can meaningfully improve your deployment without requiring prohibitively large effort.
However, research indicates 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 [best-practices](https://se-ml.github.io){ target=_blank }, and thus, can meaningfully improve your deployment without requiring prohibitively large effort.
<div style="display: flex; justify-content: space-evenly;" markdown>
[:fontawesome-brands-python: Find it on PyPI](https://pypi.org/project/great-ai/){ .md-button .md-button--primary }
[:fontawesome-brands-python: Find it on PyPI](https://pypi.org/project/great-ai){ .md-button .md-button--primary }
[:fontawesome-brands-docker: Find it on DockerHub](https://hub.docker.com/repository/docker/schmelczera/great-ai){ .md-button .md-button--primary }
</div>
## Production use
GreatAI has been battle-tested on the core platform services of [ScoutinScience](https://www.scoutinscience.com/){ target=_blank }.
![ScoutinScience logo](media/scoutinscience.svg#only-light)
![ScoutinScience logo](media/scoutinscience-white.svg#only-dark)