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 Workspaces
02. Prerequisites & Other Requirements
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.
