Edge AI for IoT Developers Specialization

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.

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.

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.