Edge AI for IoT Developers Specialization

Edge AI for IoT Developers Specialization

Bootcamp AI

About This Course

3 months program

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.

 

Certificate

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Curriculum

230 Lessons

Program Introduction

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
01. Notebooks and WorkspacesPreview
02. Prerequisites & Other RequirementsPreview
03. Notebooks and Workspaces0:18

Introduction to AI at the Edge

Leveraging Pre-Trained Models

The Model Optimizer

The Inference Engine

Deploying an Edge App

Project Deploy a People Counter App at the Edge

Introduction to Hardware at the Edge

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.

CPUs and Integrated GPUs

VPUs

FPGAs

Project Smart Queuing System

Introduction to Software Optimization

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.

Reducing Model Operations

Reducing Model Size

Other Optimization Tools and Techniques

Project Computer Pointer Controller

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IoT
Level
Intermediate
Lectures
230 lectures

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