robotUprising_Azure.md

Robot Uprising - Microsoft Azure

Activate Azure

You can activate a free Microsoft Azure account at http://azure.com/free

For the purpose of the hackathon, we're providing you with free Azure Pass codes worth 100$ and valid for 30-days.

Activate your Azure Pass

Instructions

• Do not use your school or company email address. Use personal emails (Outlook, Live, Hotmail, Gmail).
• Do not use the pass on an account that has already used a pass before. These can be activated only once per account.
• This is valid only for 30 days.
• Open an In-Private window when starting this process, to ensure you're not logged in already with some other account.

Don't have an Microsoft-account? You can create a new account at http://account.live.com

Get your unique Azure Pass code here and activate the account at www.microsoftazurepass.com

Workshop material

1. Building a custom image classification solution with Custom Vision Service (link) For the sake of time, choose the provision custom vision services in Azure (not the notebooks version)

2. Setup your development envrionement for Linux devices. (link) Make sure you have Git, Visual Studio Code and .NET Core 2.1 SDK and Docker installed. Then follow this guide to provision the right services to run a simulated IoT Edge device (a Linux virtual machine on Azure).

3. (optional) Develop custom code modules with C (link), C# (link), Java (link), Node.js (link) to learn how to deploy code that implements app logic directly on the IoT Edge Ddevice. In this tutorial you'll learn to create and deploy and IoT Edge module that filters simulated sensor data.

4. Tutorial: Perform image classification at the edge with Custom Vision Service (link) with C#. In this tutorial you'll learn to Export a model from CustomVision.ai and deploy it as a module for an IoT Edge device.

Other resources

• Tutorial: Get started creating your first ML experiment with the Python SDK (link) In this tutorial, you'll get started with the Azure Machine Learning Python SDK running in Jupyter notenbooks. You'll learn to create a workspace to manage your experiments and machine learning models, train multiple machine learning models and learn about model management using both the Azure Portal and the SDK.

  • Tutorial: Train image classification models with MNIST data and scikit-learn using Azure Machine Learning
  • Tutorial: Use automated machine learning to predict taxi fares
  • Learn how using the Open Neural Network Exchange (ONNX) can help optimize the inference of your machine learning model

• Get familiar with Stream Analytics (link) for a real-time analytics and complex event-processing engine. An Azure Stream Analytics job consists of an input, query, and an output.

  • Start with Analyze phone call data with Stream Analytics and visualize results in Power BI dashboard
  • Continue with Process data from IoT Devices

• Working with storage on Azure (link) to understand various Azure Storage options (Blogs, Files, Queues and Tables). Check Deciding when to use Azure Blobs, Azure Files, or Azure Disks for a quick cheatsheet.

  • Get familiar with Cosmos DB for a globally distributed, multi-model NoSQL database service that will allow you to reach low latency and high availability. Elastically scale throughput and storage, and take advantage of fast, single-digit-millisecond data access using the APIs of your choice, including SQL, MongoDB, Cassandra, Tables or Gremlin. Quick starts available for Portal, .NET, Java, Node.js, Python and Xamarin