Learn about the IoT and Edge Computing, with Oxford University Cloud Microsoft Learn learning path

Published Apr 20 2020 03:45 AM 2,986 Views
Microsoft

 

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Oxford University IoT and Edge Computing learning path is live on Microsoft Learn. In partnership with Ajit Jaokar and Oxford University.

In this Microsoft Learn learning path, we take an interdisciplinary engineering approach. We aspire to create a standard template for many complex areas for deployment of AI on edge devices such as Drones, Autonomous vehicles etc. The learning path presents implementation strategies for an evolving landscape of complex AI applications. Containers are central to this approach. When deployed to edge devices, containers can encapsulate deployment environments for a range of diverse hardware. CICD (Continuous integration - continuous deployment) is a logical extension to deploying containers on edge devices. In future modules in this learning path, we may include other techniques such as serverless computing and deployment on Microcontroller Units.

 

The engineering-led approach underpins themes / pedagogies for engineering education such as 

  • Systems thinking
  • Experimentation and Problem solving
  • Improving through experimentation
  • Deployment and analysis through testing
  • Impact on other engineering domains
  • Forecasting behaviour of a component or system
  • Design considerations
  • Working within constraints/tolerances and specific operating conditions – for example, device constraints
  • Safety and security considerations
  • Building tools which help to create the solution
  • Improving processes - Using edge(IoT) to provide an analytics feedback loop to the business process to drive processes
  • The societal impact of engineering
  • The aesthetical impact of design and engineering
  • Deployments at scale
  • Solving complex business problems by an end-to-end deployment of AI, edge, and cloud.

Ultimately, AI, cloud, and edge technologies deployed as containers in CICD mode can transform whole industries by creating an industry-specific, self-learning ecosystem spanning the entire value chain. We aspire to design such a set of templates/methodologies for the deployment of AI to edge devices in the context of the cloud.

 

In this learning path, you will:

  • Learn about creating solutions using IoT and the cloud
  • Understand the process of deploying IoT based solutions on edge devices
  • Learn the process of implementing models to edge devices using containers
  • Explore the use of DevOps for edge devices

Explain the significance of Azure IoT and the problems it solves. Describe Azure IoT components and explain how you combine them to solve IoT solutions, which create value for enterprises.

In this module, you will:

  • Evaluate whether Azure IoT can address the problems associated with large-scale IoT deployment
  • Describe how the components of Azure IoT work together to build a cloud-based IoT solution

 

Assess the characteristics of Azure IoT Hub and determine scenarios when to use IoT Hub.

In this module, you will:

  • Evaluate whether IoT Hub can effectively address the problems associated with large-scale IoT deployment
  • Describe how the components of IoT hub work together to build IoT applications managed through the cloud

 

Explain the essential characteristics of the IoT Edge and the functionality of the IoT Edge components (modules, runtime, and cloud interface). Characterize the types of problems that you can solve with IoT Edge. Describe how the elements of IoT Edge can be combined to solve the problem of deploying IoT applications in the cloud.

In this module, you will:

  • Evaluate situations where IoT Edge can help in deploying IoT applications to the cloud
  • Describe the components of IoT Edge
  • List the capabilities of the IoT Edge for the IoT solutions in the cloud

 

Deploy a pre-built temperature simulator module to the edge using a container. The pre-built module will be deployed to an IoT edge device. You will check that the module was successfully created and deployed to the edge. You will view the simulated data from the deployed module.

In this module, you will:

  • Launch a module from Azure portal to IoT Edge
  • Generate simulated data from an edge device
  • Verify data generated from the edge device

 

Train and package an Azure machine learning module for deployment to IoT Edge device https://docs.microsoft.com/en-us/learn/modules/train-package-module-iot-edge/ 

Deploy a trained machine learning module to the edge using a container. The machine learning module you create will be deployed to an IoT Edge device. You'll check that your container image was successfully created and stored in the Azure container registry. You'll view the data from the deployed module from the IoT Edge.

In this module, you will:

  • Launch a module from Azure portal to IoT Edge using a container
  • Generate simulated data from an edge device
  • Verify data generated from the edge device

The complete list of all Microsoft Learn, IOT Modules are available at https://docs.microsoft.com/en-us/learn/browse/?products=azure-iot%2Cazure-iot-central%2Cazure-iot-ed...

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