Edge AI for IoT Developers Specialization

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.
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Level

ESTIMATED TIME

3 months complete

8 – 10 hours / week

English

What will you learn

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.
Prerequisites
This program requires intermediate knowledge of Python, and experience with Deep Learning, Command Line, and OpenCV.
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

 

PROJECT
Deploy a People Counter at the Edge
Investigate different pre-trained models for person detection, and detect the number of people in the frame, and the time spent there.

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.

 

PROJECT
Design a Smart Queuing System
Build custom queuing systems for the retail, manufacturing and transportation sectors and use the Intel® DevCloud for the Edge to test your solutions performance.

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.

 

PROJECT
Build a Computer Pointer Controller
Use models available in the OpenVINO™ toolkit to control your computer pointer using your eye gaze.

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Program Offer It includes
Real world projects Yes
STUDENT SERVICES
Mentor Tech Support Yes
Student community Yes
CAREER SERVICES
CV support Yes
Freelance Projects Yes
Real World Project
Develop an End-to-End project that will allow you to interact in a real work environment.

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Edge AI for IoT Developers Specialization

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