Intel® Edge AI for IoT Developers

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What is the course about?

Intel® Edge AI for IoT Developers

The course Intel® Edge AI for IoT Developers is an online class provided by Udacity. It may be possible to receive a verified certification or use the course to prepare for a degree.

Lead the development of cutting-edge Edge AI applications for the future of the Internet of Things. Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision & deep learning inference applications.

Course description

Intel® Edge AI for IoT Developers
3 Months (10 hours / week)

Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications, and run pre-trained deep learning models for computer vision on-premise. You will identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU), and utilize the Intel® DevCloud for the Edge to test model performance on the various hardware types. Finally, you will use software tools to optimize deep learning models to improve performance of Edge AI systems.
Lead the development of cutting-edge Edge AI applications that are the future of the Internet of Things. Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications.
This program requires intermediate knowledge of Python, and experience with Deep Learning, Command Line, and OpenCV.See detailed requirements.
Leverage a pre-trained model for computer vision inferencing. You will convert pre-trained models into the framework agnostic intermediate representation with the Model Optimizer, and perform efficient inference on deep learning models through the hardware-agnostic Inference Engine. Finally, you will deploy an app on the edge, including sending information through MQTT, and analyze model performance and use cases
Grow your expertise in choosing the right hardware. Identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU). Utilize the Intel® DevCloud for the Edge to test model performance and deploy power-efficient deep neural network inference on on the various hardware types. Finally, you will distribute workload on available compute devices in order to improve model performance.
Learn how to optimize your model and application code to reduce inference time when running your model at the edge. Use different software optimization techniques to improve the inference time of your model. Calculate how computationally expensive your model is. Use the DL Workbench to optimize your model and benchmark the performance of your model. Use a VTune amplifier to find and fix hotspots in your application code. Finally, package your application code and data so that it can be easily deployed to multiple devices.
For Python Experience: AI Programming with Python For Deep Learning Experience: Deep LearningFor AI Modeling: Intro to Machine Learning with Pytorch or Intro to Machine Learning with TensorFlow For Computer Vision Experience: Computer Vision
The Edge AI market is forecasted to grow from $355 million in 2018 to $1.15 billion by 2023, at 27% annually. (MarketsandMarkets)

Edge AI Fundamentals with OpenVINO™
Leverage a pre-trained model for computer vision inferencing. You will convert pre-trained models into the framework agnostic intermediate representation with the Model Optimizer, and perform efficient inference on deep learning models through the hardware-agnostic Inference Engine. Finally, you will deploy an app on the edge, including sending information through MQTT, and analyze model performance and use cases
Deploy a People Counter at the Edge
Hardware for Computer Vision & Deep Learning Application Deployment
Grow your expertise in choosing the right hardware. Identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU). Utilize the Intel® DevCloud for the Edge to test model performance and deploy power-efficient deep neural network inference on on the various hardware types. Finally, you will distribute workload on available compute devices in order to improve model performance.
Design a Smart Queuing System
Optimization Techniques and Tools for Computer Vision & Deep Learning Applications
Learn how to optimize your model and application code to reduce inference time when running your model at the edge. Use different software optimization techniques to improve the inference time of your model. Calculate how computationally expensive your model is. Use the DL Workbench to optimize your model and benchmark the performance of your model. Use a VTune amplifier to find and fix hotspots in your application code. Finally, package your application code and data so that it can be easily deployed to multiple devices.
Build a Computer Pointer Controller
This program requires intermediate knowledge of Python, and experience with Deep Learning, Command Line, and OpenCV.

70% of data being created is at the edge, and only half of that will go to the public cloud; the rest will be stored and processed at the edge, which requires a different kind of developer. Demand for professionals with the Edge AI skills will be immense, as the Edge Artificial Intelligence (AI) software market size is forecasted to grow from $355 Million in 2018, to $1.15 billion by 2023, at an Annual Growth Rate of 27%.(MarketsandMarkets) In the Edge AI for IoT Developers Nanodegree program, you’ll leverage the potential of edge computing and use the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications.
Computer Vision is a fast-growing technology being deployed in nearly every industry from factory floors to amusement parks to shopping malls, smart buildings, and smart homes. It is also driving the evolution of machine learning and human interactions with intelligent systems. Additional applications include drones, security cameras, robots, facial recognition on cell phones, self-driving vehicles, and more, which means these industries and more all need developers with computer vision and deep learning IoT experience.
This Nanodegree program will prepare you for roles such as IoT Developer, IoT Engineer, Deep Learning Engineer, Machine Learning Engineer, AI Specialist, VPU/CPU/FPGA Developer and more for companies and organizations looking to innovate their hardware on the Edge.
If you are an enterprise developer and/or professional developer interested in advanced learning, specifically deep learning and computer vision, this program is right for you.
Additionally if you have a background as an IoT Application Prototyper, IoT Application Implementer, IoT System Prototyper, or an IoT System Implementer, or in heterogeneous architectures as a Device Developer, Application Prototyper, Algorithm Developer, Solution Developer, or in security as an Architect/Planner, Security Specialist, or a Protocol Implementer, this program is a good fit.
What is Edge AI? What are some applications of this technology?
Edge Computing runs processes locally on the device itself, instead of running them in the cloud. This reduced computing time allows data to be processed much faster, removes the security risk of transferring the data to a cloud-based server, and reduces the cost of data transfer, as well as the risks of bandwidth outages disrupting performance.
Computer vision and AI at the edge are becoming instrumental in powering everything from factory assembly lines and retail inventory management, to hospital urgent care medical imaging equipment like X-ray and CAT scans. Drones, security cameras, robots, facial recognition on cell phones, self-driving vehicles, and more all utilize this technology as well.
According to IEEE Innovation at Work, “By 2020, approximately 20+ billion devices will likely be connected via the Internet of Things (IoT), creating incredible amounts of data every minute. The time it takes to move data to the cloud, perform service on it and then move it back to devices is far too long to meet the increasing needs of the IoT. Unlike cloud computing, which relies on a single data center, edge computing works with a more distributed network, eliminating the round-trip journey to the cloud and offering real-time responsiveness and local authority. It keeps the heaviest traffic and processing closest to the end-user application and devices – smartphones, tablets, home security systems, and more – that generate and consume data. This dramatically reduces latency and leads to real-time, automated decision-making.” ( IEEE)
What is the InteI® DevCloud for the Edge?
The Intel® DevCloud for the Edge allows you to actively prototype and experiment with AI workloads for computer vision on Intel® hardware.
You have full access to hardware platforms hosted in our cloud environment, designed specifically for deep learning. You can test the performance of your models using the Intel® Distribution of OpenVINO™ Toolkit and combinations of CPUs, GPUs, VPUs such as the Intel® Neural Compute Stick 2 (NCS2) and FPGAs, such as the Intel® Arria® 10. The Intel® DevCloud for the Edge contains a series of Jupyter* notebook tutorials and examples preloaded with everything you needed to quickly get started.
This includes trained models, sample data and executable code from the Intel® Distribution of OpenVINO™ Toolkit as well as other tools for deep learning. These notebooks are designed to help you quickly learn how to implement deep learning applications to enable compelling, high-performance solutions. Intel® has AI hardware waiting for your prototyping of edge inference jobs.
No hardware setup is required on your end. The Intel® DevCloud for the Edge utilizes Jupyter* Notebooks to execute code directly within the Web browser. Jupyter* is a browser-based development environment which allows you to run code and immediately visualize results. You can prototype innovative computer vision solutions in our cloud environment, then execute your code on any of Intel’s® available combination of hardware resources.

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Prerequisites & Facts

Intel® Edge AI for IoT Developers

Course Topic

AI, Computer Science, IoT

University, College, Institution

Udacity

Course Skill Level

see course summary

Course Language

English

Place of class

Online, self-paced (see curriculum for more information)

Degree

Certificate

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Degree & Cost

Intel® Edge AI for IoT Developers

To obtain a verified certificate from Udacity you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate (399 USD) or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.

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Intel® Edge AI for IoT Developers

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