Intel® Edge AI for IoT Developers

70% of data being created is at the edge, and only half of that will go to the public cloud; the rest will be stored and processed at the edge, which requires a different kind of developer. Demand for professionals with the Edge AI skills will be immense, as the Edge Artificial Intelligence (AI) software market size is forecasted to grow from $355 Million in 2018, to $1.15 billion by 2023, at an Annual Growth Rate of 27%.( ) In the Edge AI for IoT Developers Nanodegree program, you'll leverage the potential of edge computing and use the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications.
Computer Vision is a fast-growing technology being deployed in nearly every industry from factory floors to amusement parks to shopping malls, smart buildings, and smart homes. It is also driving the evolution of machine learning and human interactions with intelligent systems. Additional applications include drones, security cameras, robots, facial recognition on cell phones, self-driving vehicles, and more, which means these industries and more all need developers with computer vision and deep learning IoT experience.

This Nanodegree program will prepare you for roles such as IoT Developer, IoT Engineer, Deep Learning Engineer, Machine Learning Engineer, AI Specialist, VPU/CPU/FPGA Developer and more for companies and organizations looking to innovate their hardware on the Edge.

If you are an enterprise developer and/or professional developer interested in advanced learning, specifically deep learning and computer vision, this program is right for you.
Additionally if you have a background as an IoT Application Prototyper, IoT Application Implementer, IoT System Prototyper, or an IoT System Implementer, or in heterogeneous architectures as a Device Developer, Application Prototyper, Algorithm Developer, Solution Developer, or in security as an Architect/Planner, Security Specialist, or a Protocol Implementer, this program is a good fit.

Edge Computing runs processes locally on the device itself, instead of running them in the cloud. This reduced computing time allows data to be processed much faster, removes the security risk of transferring the data to a cloud-based server, and reduces the cost of data transfer, as well as the risks of bandwidth outages disrupting performance.
Computer vision and AI at the edge are becoming instrumental in powering everything from factory assembly lines and retail inventory management, to hospital urgent care medical imaging equipment like X-ray and CAT scans. Drones, security cameras, robots, facial recognition on cell phones, self-driving vehicles, and more all utilize this technology as well.
According to IEEE Innovation at Work, "By 2020, approximately 20+ billion devices will likely be connected via the Internet of Things (IoT), creating incredible amounts of data every minute. The time it takes to move data to the cloud, perform service on it and then move it back to devices is far too long to meet the increasing needs of the IoT. Unlike cloud computing, which relies on a single data center, edge computing works with a more distributed network, eliminating the round-trip journey to the cloud and offering real-time responsiveness and local authority. It keeps the heaviest traffic and processing closest to the end-user application and devices – smartphones, tablets, home security systems, and more – that generate and consume data. This dramatically reduces latency and leads to real-time, automated decision-making." ( )

The Intel® DevCloud for the Edge allows you to actively prototype and experiment with AI workloads for computer vision on Intel® hardware.
You have full access to hardware platforms hosted in our cloud environment, designed specifically for deep learning. You can test the performance of your models using the Intel® Distribution of OpenVINO™ Toolkit and combinations of CPUs, GPUs, VPUs such as the Intel® Neural Compute Stick 2 (NCS2) and FPGAs, such as the Intel® Arria® 10. The Intel® DevCloud for the Edge contains a series of Jupyter notebook tutorials and examples preloaded with everything you needed to quickly get started.
This includes trained models, sample data and executable code from the Intel® Distribution of OpenVINO™ Toolkit as well as other tools for deep learning. These notebooks are designed to help you quickly learn how to implement deep learning applications to enable compelling, high-performance solutions. Intel® has AI hardware waiting for your prototyping of edge inference jobs.
No hardware setup is required on your end. The Intel® DevCloud for the Edge utilizes Jupyter Notebooks to execute code directly within the Web browser. Jupyter is a browser-based development environment which allows you to run code and immediately visualize results. You can prototype innovative computer vision solutions in our cloud environment, then execute your code on any of Intel's® available combination of hardware resources.

You are able to deploy high performance, deep learning inference with the Intel® Distribution of OpenVINO™ Toolkit.
The Intel® Distribution of OpenVINO™ Toolkit allows you to harness the full potential of AI and computer vision across multiple Intel® architectures to enable new and enhanced use cases in health and life sciences, retail, industrial and more. Develop applications and solutions that emulate human vision with the Intel® Distribution of OpenVINO™ toolkit. Based on convolutional neural networks (CNN), the toolkit extends workloads across Intel® hardware (including accelerators) and maximizes performance.

• Enables deep learning inference from edge to cloud
• Accelerates AI workloads, including computer vision, audio, speech, language, and recommendation systems
• Supports heterogeneous execution across Intel® architecture and AI accelerators—CPU, iGPU, Intel® Movidius™ Vision Processing Unit (VPU), FPGA, and Intel® Gaussian & Neural Accelerator (Intel® GNA)—using a common API
• Speeds up time to market via a library of functions and pre-optimized kernels
• Includes optimized calls for OpenCV, OpenCL™ kernels, and other industry tools and libraries

The DL Workbench, part of the Intel® Distribution of OpenVINO™ Toolkit, is a web-based graphical environment that enables users to visualize a simulation of performance of deep learning models and datasets on various Intel® architecture configurations (CPU, GPU, VPU). In addition, users can automatically fine-tune the performance of an Intel® Distribution of OpenVINO™ Toolkit model by reducing the precision of certain model layers (calibration) from FP32 to INT8. Additional tuning algorithms will be supported in future releases.

The Intel® Distribution of OpenVINO™ Toolkit is for developers looking to deploy deep learning models on hardware with Intel® chips. Students will be able to interact with Intel’s® IoT development platform to optimize the performance of their hardware using the DL Workbench. Through Udacity's interactive workspaces, you'll be able to send jobs to Intel® DevCloud for the Edge and see how different hardware performs in real time.
Deploying AI models on the Edge requires a particular set of tools that providers such as Intel® have built. Through Udacity’s hands-on exercises that integrate with Intel’s® platform, students will be able to actually practice testing AI model performance on hardware without needing access to the hardware.
The Intel® DevCloud for the Edge is a cloud-based platform that lets you deploy machine learning models on hardware in the cloud before you purchase the actual hardware so you test and compare the performance of different hardware.