Udacity – Intel® Edge AI for IoT Developers 2020
Edge AI for IoT Developers

Udacity – Intel® Edge AI for IoT Developers 2020


Intel® Edge AI for IoT Developers is the name of the Intel® Edge AI for IoT Developers course published by the prestigious Udacity Academy. What is Edge AI? What are the uses of this technology?

Edge computing runs processes locally on the device instead of in the cloud. This reduction in computing time allows data to be processed much faster, eliminates the security risk of transferring data to a cloud-based server, and lowers the cost of data transfer as well as the risk of bandwidth outages.

It disrupts performance. Computer vision and artificial intelligence are at the edge to power everything from factory assembly lines and retail inventory management to hospital urgent care medical imaging equipment such as X-ray and CAT scans. Drones, security cameras, robots, facial recognition in cell phones, self-driving vehicles, and more all use this technology.

According to IEEE Innovation at Work, “By 2020, approximately 20 billion devices will likely be connected via the Internet of Things (IoT), generating incredible amounts of data every minute. The amount of time it takes to transfer data to the cloud is what the service does. on it and then bringing it back to the devices is too long to meet the growing needs of the Internet of Things. 

Unlike cloud computing, which relies on a single data center, edge computing works with a more distributed network, eliminating round-trips. Cloud and provide real-time accountability and local authority. It keeps the heaviest traffic and processing closer to the application and end-user devices—smartphones, tablets, home security systems, and more—that generate and consume data. This reduces latency significantly. And it leads to real-time and automated decision-making.” (IEEE)

70% of data generated is at the Edge, and only half of that goes to the public cloud. The rest will be stored and processed in Edge, which requires a different kind of developer. Demand for professionals with Edge Artificial Intelligence (AI) skills will be high as the Edge Artificial Intelligence (AI) software market size is projected to grow from $355 million in 2018 to $1.15 billion by 2023 at a CAGR of 27%.
In the Nanodegree Edge AI for IoT Developers, you harness the potential of edge computing and use the Intel® Distribution of OpenVINO™ Toolkit to fast-track the development of high-performance computer vision and deep learning inference applications.

What jobs does this training course prepare me for? This Nanodegree program prepares you for roles such as IoT Developer, IoT Engineer, Deep Learning Engineer, Machine Learning Engineer, Artificial Intelligence Specialist, VPU/CPU/FPGA Developer, and more for companies and organizations looking to innovate their hardware at the edge. prepares

What you will learn in the Intel® Edge AI for IoT Developers course:

  • Use a pre-trained model for computer vision inference 
  • Convert pre-trained models to a framework agnostic
  • Perform efficient inference on deep learning models
  • Create an application in Edge, including sending information via MQTT and analyzing performance and using case models
  • The importance of choosing the right hardware and the process involved in doing so
  • Identify the key specifications of Intel® processors and GPUs 
  • Use Intel® Devcloud for the Edge to run deep learning  on integrated CPUs and GPUs
  • Identify the key specifications of Intel® VPUs
  •  Use Intel® DevCloud for the Edge to run deep learning on VPU
  • Identify the key features of Intel® FPGAs
  • And…..

Course details

Publisher:  Udacity
Instructor: Stewart Christie , Michael Virgo, Soham , Soham Chatterjee , Vaidheeswaran Archana
Language: English
Education level: Intermediate
Number of courses: 12
Duration of training: Assuming 10 hours of work per week, about 3 months

Course chapters:

Deploy a People Counter App at the Edge

  • Leveraging Pre-Trained Models
  • The Model Optimizer
  • The Inference Engine
  • Deploying An Edge App

Hardware for Computer Vision & Deep Learning Application Deployment

  • Introduction to Hardware at the Edge
  • CPU and Integrated GPU
  • Vision Processing Units
  • Field Programmable Gate Arrays

Optimization Techniques and Tools for Computer Vision Deep Learning Applications

  • Introduction to Software Optimization
  • Reducing Model Operations
  • Reducing Model Size
  • Other Software Optimization Techniques

Intel® Edge AI for IoT Developers course prerequisites:

  • Intermediate knowledge of programming in Python
  • Experience with training and deploying deep learning models
  • Familiarity with different DL layers and architectures (CNN based)
  • Familiarity with the command line (bash terminal)
  • Experience using OpenCV

Course pictures:

Edge AI for IoT Developers

Installation guide

In order to view the courses of the course in an organized and regular way, run the index.html file and run the videos through this file.

English subtitle

Quality: 720p

download link

Download part 1 – 1.38 GB
file password link
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