Responsible innovation is top of mind. As such, the tech industry as well as a growing number of organizations of all kinds in their digital transformation are being called upon to develop and deploy (non-Generative vs. Generative) Artificial Intelligence (AI) technologies and Machine Learning (ML)-powered systems (products or services) and/or features (all referred as to AI systems below) more responsibly. And yet many organizations implementing such AI systems report being unprepared to address AI risks and failures, and struggle with new challenges in terms of governance, security and compliance.
Advancements in AI, and more specifically in Generative AI, are indeed different than other technologies because of the pace of innovation. There has been hundreds of research papers published every year in the past few years -, but also because of its proximity to human intelligence, impacting us at a personal and societal level.
There are a number of challenges and questions raised through the use of AI technologies. We refer to these as socio-technical impacts. All of these have given rise to an industry debate about how the world should/shouldn't use these new capabilities. It isn't because you can do something that you should necessarily do it.
This project is an attempt to introduce and illustrate the use of:
- Resources designed to help you responsibly use (non-Generative vs. Generative) AI at every stage of innovation - from ideation to design, development, deployment, and beyond.
- Available toolkits & frameworks that help you integrate relevant Responsible AI features & guardrails into your (non-Generative vs. Generative) AI environment by themes and through the lifecycle stages of your AI system (MLOps vs. LLMOps, i.e., MLOps for LLMs).
- Activities to strengthen gradually the confidence that we can have in these technologies and therefore facilitate its adoption in contexts where it would have a great responsibility.
It is thus designed to help you or your "customers", whoever they are, to put Responsible AI into practice for your AI-powered solutions throughout their lifecycle.
This project is a work in progress (WIP).
This project currently contains the following modules:
- Towards your own Responsible AI journey
- Building and using Generative AI responsibly
- Responsible AI Tooling Tutorials for Non-Generative AI
- End-to-End Responsible AI Lifecycle Walkthrough for Non-Generative AI
- Towards a (more) trustworthy AI lifecycle
Each of the above modules consists of a series of guide(s), presentation(s), and tutorial(s)/walkthrough(s) for data engineers, data scientists, ML developers, ML engineers, and other AI practitioners, as well as potentially anyone interested considering the wide range of socio-technical aspects involved in the subject.
Besides the guides and presentations shared as part of it, the workshop is also meant to provide hands-ons. Therefore, basic knowledge of any version of Python is a prerequisite. It also assumes for some of the tutorials that you have prior experience training machine learning (ML) models with Python using open-source frameworks like Scikit-Learn, PyTorch, and TensorFlow.
Before you begin, you will need to clone this repos and also setup your execution environment:
- Various options are available to clone this repo. See Cloning the repo for more information.
- Regarding your execution environment, and the (open-source) software and (Azure AI) services being featured throughout the workshop, see Fulfilling the prerequisites for more information.
This project welcomes contributions and suggestions. (For advices on editing contents so that they remain consistent and optimized for readers' understanding, see note.)
Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct.
For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
Microsoft and any contributors grant you a license to the Microsoft documentation and other content in this repository under the Creative Commons Attribution 4.0 International Public License, see the LICENSE file, and grant you a license to any code in the repository under the MIT License, see the LICENSE-CODE file.
Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries. The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks. Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653.
Privacy information can be found at https://privacy.microsoft.com/en-us/.
Microsoft and any contributors reserve all other rights, whether under their respective copyrights, patents, or trademarks, whether by implication, estoppel or otherwise.