alejandrolmeida / bootcamp-2022

Global AI Bootcamp 2022

Januari 14 until March 31.

In this GitHub repository you find all the information around the Global AI Bootcamp 2022.

Resources:

• Logos and artwork
• Event page
• Organizers Information
• Workshops
• Speakers Presentation

Event format

The event is all about being together, in-person or virtual, to learn everything about Artificial intelligence.

Organizers information

Support provided

• Azure Passes
• 4 Workshops
• Global Keynote
• Streaming Support
• MeetUp Pro subscription

How to join

• Login or Create an account on the Global AI Website
• Create a local Community with the city or country name you are in
• Create a new event
• Join the Global AI Community Slack

If applicable

• Request Azure Passes
• Request event support
• Ask a question

Content

Keynote

-You can watch the keynote on YouTube or download it here.

Workshops / Hands-on Labs / Presentations

Workshop 1: The Azure Function that can see

Could have imagined a few years ago that you would be able to create an Azure Function that can tell you what is on your images? Good news, this is now possible with a few lines of code and some sample images. In this hands-on lab you are going to train a ONNX classification Model with the Custom Vision service that will run in an Azure Function. The language of choice is Python and you will use Visual Studio code as the editor and the Azure CLI to manage and create your Azure Resources. At the end of the lab you have a serverless API that can classify Images and learned about how to use the Azure Custom Vision Python SDK to train an ONNX model.

Workshop content

Workshop 2: Cognitive Search

In this hands-on lab you are going to work with Cognitive Search.

Workshop content

Workshop 3: Bot Composer

In this hands-on lab you are going to work with Bot Composer.

Workshop content

Workshop 4: Train and deploy a PyTorch model using the Azure Machine Learning platform.

In this hands-on lab you are going to build and deploy your own trained vision model to a highly scalable endpoint using Azure Machine Learning. You start with setting up your cloud workspace and learn how to manage your data and make it reusable. Next you will train a PyTorch model using the transfer learning approach and finally you deploy the model wrapped in an API in a managed endpoint. At the end of this hands-on lab you have gone through the complete life-cycle of a model, from data to deployment using the Azure Machine Learning platform.

Workshop content