Edge AI applications are revolutionizing the IoT industry by bringing fast, intelligent behavior to the locations where it is needed. In this Nanodegree program, you will learn how to develop and optimize Edge AI systems, using the Intel® Distribution of OpenVINO™ Toolkit. A graduate of this program will be able to:
- Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications.
- Run pre-trained deep learning models for computer vision on-prem.
- Identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU).
- Utilize Intel® DevCloud for the Edge to test model performance on various hardware types (CPU, VPU, FPGA, and Integrated GPU).
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 1: Deploy a People Counter App 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.
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 2: 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.
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 3: Build a Computer Pointer Controller
- Use models available in the OpenVINO™ toolkit to control your computer pointer using your eye gaze.