ABSTRACT

Artificial intelligence (AI) stands out as a transformational technology of the digital age. Its practical applications are growing very rapidly. One of the chief reasons AI applications are attaining prominence, is in its design to learn continuously, from real-world use and experience, and its capability to improve its performance. It is no wonder that the applications of AI span from complex high-technology equipment manufacturing to personalized exclusive recommendations to end-users. Many deployments of AI software, given its continuous learning need, require computation platforms that are resource intense, and have sustained connectivity and perpetual power through central electrical grid.

In order to harvest the benefits of AI revolution to all of humanity, traditional AI software development paradigms must be upgraded to function effectively in environments that have resource constraints, small form factor computational devices with limited power, devices with intermittent or no connectivity and/or powered by non-perpetual source or battery power.

The aim this book is to prepare current and future software engineering teams with the skills and tools to fully utilize AI capabilities in resource-constrained devices. The book introduces essential AI concepts from the perspectives of full-scale software development with emphasis on creating niche Blue Ocean small form factored computational environment products.

section Section-I|80 pages

Introduction to Artificial Intelligence & Frameworks

chapter Chapter 1|47 pages

Introduction

chapter Chapter 2|33 pages

Standard Processes and Frameworks

section Section-II|109 pages

Data Sources and Engineering Tools

chapter Chapter 3|41 pages

Data—Call for Democratization

chapter Chapter 4|54 pages

Machine Learning Frameworks and Device Engineering

chapter Chapter 5|14 pages

Device Software and Hardware Engineering Tools

section Section-III|141 pages

Model Development and Deployment

chapter Chapter 6|106 pages

Supervised Models

chapter Chapter 7|35 pages

Unsupervised Models

section Section-IV|18 pages

Democratization & Future of AI

chapter Chapter 8|17 pages

National Strategies

chapter Chapter 9|1 pages

Future