GoDataFest Azure Machine Learning Workshops
Workshop 1: Custom Vision AI - Creating applications that can see, hear, speak or understand - using Microsoft Cognitive Services - Predict Tools
In this workshop you will be introduced to the Microsoft Azure Cognitive Services, a range of offerings you can use to infuse intelligence and machine learning into your applications without needing to build the code from scratch. We will cover pre-trained AI APIs, such as computer vision, that are accessed by REST protocol. Next we will dive into Custom AI that uses transfer learning - Microsoft Azure Custom Vision. This enables you to provide a small amount of your own data to train an image classification model. Wrapping the workshop up by building our custom trained AI into an application - using Logic Apps, this technology is ideal for building data pipeline processes that work with your machine learning models.
Pre-requisites for your machine:
- Clone this repository to your local machine to gain images and code samples you need for this workshop
- Microsoft Azure Pass or Subscription - get a free trial here
- Laptop with a modern web browser (Google Chrome, Microsoft Edge)
- Postman, API Development Environment - available on Windows, Linux and macOS
All demos and content have been tested on a Windows PC, however all options should run from macOS and Linux machines as well. Please provide information via an issue or pull request if you have feedback on other operating systems
Go to Workshop 1: Custom Vision AI
In this workshop, you will learn how to build a machine learning model to predict Wine Quality with the visual interface for Azure Machine Learning Service. We will use an example case study, with the Data Science Lifecycle as guideline. You will first look at the Business Understanding, proceed with the Data Acquisition and Understanding. Then you will start the Modeling part, the Deployment, and finally the Customer Acceptance part. We will first introduce every step, and then elaborate on the corresponding visual interface components to fulfil the step.
Go to Workshop 2: Visual Interface for Azure Machine Learning Service
In this workshop, you will learn how to build a machine learning model with the visual interface for Automated ML. You will use the same data from the prior workshop.
Go to Workshop 3: Visual Interface for Automated ML
In this hands on lab you are going to use Azure Machine Learning Service and Azure Notebooks to create a MNIST model and run in in a container in Azure.
Go to Bonus track: Azure Machine Learning Service and Azure Notebooks