Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

Overview

Responsible AI Workshop

Workshop logo

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 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 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 AI at every stage of innovation - from concept to development, deployment, and beyond.
  • Available toolkits & frameworks that help you integrate relevant Responsible AI features into your AI environment by themes and through the lifecycle stages of your AI system.

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.

Workshop Tutorials/Walkthroughs

Work in Progress

This project is a work in progress (WIP).

This project currently contains the following tutorials:

Each of the above tutorials consists of a series of modules 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.

Prerequisites

The workshop is meant to be hands-on. Therefore, basic knowledge of any version of Python is a prerequisite. It also assumes that you have prior experience training machine learning (ML) models with Python using open-source frameworks like Scikit-Learn, PyTorch, and TensorFlow.

One should also note that this workshop might also be introduced by the following Microsoft Learn learning paths:

Additional resources

From holistically transforming industries to addressing critical issues facing humanity, AI is already solving some of our most complex challenges and redefining how humans and technology interact.

You can visit our Responsible AI resource center where you can find access to tools, guidelines, and additional resources that will help you create a (more) Responsible AI solution:

Contributing

This project welcomes contributions and suggestions. 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.

Legal Notices

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.

Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
Azure MLOps (v2) solution accelerators.

Azure MLOps (v2) solution accelerator Welcome to the MLOps (v2) solution accelerator repository! This project is intended to serve as the starting poi

Microsoft Azure 233 Jan 01, 2023
Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.

python-is-cool A gentle guide to the Python features that I didn't know existed or was too afraid to use. This will be updated as I learn more and bec

Chip Huyen 3.3k Jan 05, 2023
Iris-Heroku - Putting a Machine Learning Model into Production with Flask and Heroku

Puesta en Producción de un modelo de aprendizaje automático con Flask y Heroku L

Jesùs Guillen 1 Jun 03, 2022
Katana project is a template for ASAP 🚀 ML application deployment

Katana project is a FastAPI template for ASAP 🚀 ML API deployment

Mohammad Shahebaz 100 Dec 26, 2022
This repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

uber-pickups-analysis Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city Information about data set The dataset contain

B DEVA DEEKSHITH 1 Nov 03, 2021
Learning --> Numpy January 2022 - winter'22

Numerical-Python Numpy NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along

Shahzaneer Ahmed 0 Mar 12, 2022
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. 10x Larger Models 10x Faster Trainin

Microsoft 8.4k Dec 30, 2022
Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models. Solve a variety of tasks with pre-trained models or finetune them in

Backprop 227 Dec 10, 2022
A machine learning model for Covid case prediction

CovidcasePrediction A machine learning model for Covid case prediction Problem Statement Using regression algorithms we can able to track the active c

VijayAadhithya2019rit 1 Feb 02, 2022
stability-selection - A scikit-learn compatible implementation of stability selection

stability-selection - A scikit-learn compatible implementation of stability selection stability-selection is a Python implementation of the stability

185 Dec 03, 2022
🎛 Distributed machine learning made simple.

🎛 lazycluster Distributed machine learning made simple. Use your preferred distributed ML framework like a lazy engineer. Getting Started • Highlight

Machine Learning Tooling 44 Nov 27, 2022
Microsoft 5.6k Jan 07, 2023
A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search (Manhattan Distance Heuristic)

A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and the A* Search (using the Manhattan Distance Heuristic)

17 Aug 14, 2022
TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models

538 Jan 01, 2023
icepickle is to allow a safe way to serialize and deserialize linear scikit-learn models

icepickle It's a cooler way to store simple linear models. The goal of icepickle is to allow a safe way to serialize and deserialize linear scikit-lea

vincent d warmerdam 24 Dec 09, 2022
hgboost - Hyperoptimized Gradient Boosting

hgboost is short for Hyperoptimized Gradient Boosting and is a python package for hyperparameter optimization for xgboost, catboost and lightboost using cross-validation, and evaluating the results o

Erdogan Taskesen 34 Jan 03, 2023
A machine learning web application for binary classification using streamlit

Machine Learning web App This is a machine learning web application for binary classification using streamlit options this application contains 3 clas

abdelhak mokri 1 Dec 20, 2021
Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

Time series analysis today is an important cornerstone of quantitative science in many disciplines, including natural and life sciences as well as eco

Christoph Mark 129 Dec 24, 2022
Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.

Tangram Website | Discord Tangram makes it easy for programmers to train, deploy, and monitor machine learning models. Run tangram train to train a mo

Tangram 1.4k Jan 05, 2023
PyPOTS - A Python Toolbox for Data Mining on Partially-Observed Time Series

A python toolbox/library for data mining on partially-observed time series, supporting tasks of forecasting/imputation/classification/clustering on incomplete multivariate time series with missing va

Wenjie Du 179 Dec 31, 2022