March 2 until March 4 2023.
In this GitHub repository you find all the information around the Global AI Bootcamp 2023.
You will find the keynote here from February 20th. The duration of the keynote is 45 minuts.
| Title | Unlocking the Potential of AI |
| Speakers | John Montgomery (Corporate Vice President Azure AI) Bob Breynaert (Principal Program Manager, Azure OpenAI) Dona Sarkar (Director Of Technology - Accessibility at Microsoft) Sharon Lo (Principal Program Manager Ethics & Society) |
| Abstract | This keynote video features John Montgomery, Corporate Vice President of Azure AI, Bob Breynaert, Dona Sarkar and Sharon Lo. They discuss the latest developments in AI at Microsoft, including the Azure OpenAI Service. They explore how AI can be used to improve accessibility and the importance of ethics and responsibility when it comes to AI. They also discuss the challenges and opportunities that come with the development of AI. The keynote video provides a comprehensive overview of the latest developments in AI at Microsoft and the implications for the future. |
| Link | https://keynote.globalaibootcamp.com |
You will find the workshops here from February first.
Most of the forms we complete nowadays are online but there are still times when we need to complete paper based forms. There are plenty of examples, for this workshop, we've chosen a patient registration for a doctor's surgery as it's something we've all had to do at some point.
| Item | Link |
|---|---|
| Workshop | https://newpatiente2e.github.io/docs/ |
| Train the Trainer | https://youtu.be/mkiYBvKh_Gk |
A machine learning model is a black box that is difficult to debug. Traditional machine learning model performance techniques provide a little view of the true model accuracy. In addition, these techniques have a blind spot in finding where the model or data errors are. This can lead to fairness or societal bias issues. There is an increasing need for transparency to be able to understand or explain what features drove a model’s outcome for some industry regulations. This workshop will give participants hands-on learning on how to use Azure Machine Learning’s Responsible AI dashboard to debug a model using Error Analysis, Data Analysis and Explainability/Interpretability. At the end of the workshop, participants will learn best practices of using Azure Responsible AI dashboard to help ML professionals produce AI solutions that are less harmful and more trustworthy.
| Item | Link |
|---|---|
| Workshop | https://ruyakubu.github.io/rai-dashboard-workshop/ |
| Instructor Materials | https://github.com/ruyakubu/rai-workshop-instructor-materials |
| Train the Trainer | https://youtu.be/gEOuxTC_sn8 |
This workshop covers clustering with Azure Machine Learning, Automated ML, and model explainability.
| Item | Link |
|---|---|
| Workshop | https://sammydeprez.gitbook.io/azure-machine-learning-model-training/ |
| Train the trainer | https://youtu.be/FTnzwnak_XQ |