Book Image

AI Blueprints

By : Dr. Joshua Eckroth, Eric Schoen
Book Image

AI Blueprints

By: Dr. Joshua Eckroth, Eric Schoen

Overview of this book

AI Blueprints gives you a working framework and the techniques to build your own successful AI business applications. You’ll learn across six business scenarios how AI can solve critical challenges with state-of-the-art AI software libraries and a well thought out workflow. Along the way you’ll discover the practical techniques to build AI business applications from first design to full coding and deployment. The AI blueprints in this book solve key business scenarios. The first blueprint uses AI to find solutions for building plans for cloud computing that are on-time and under budget. The second blueprint involves an AI system that continuously monitors social media to gauge public feeling about a topic of interest - such as self-driving cars. You’ll learn how to approach AI business problems and apply blueprints that can ensure success. The next AI scenario shows you how to approach the problem of creating a recommendation engine and monitoring how those recommendations perform. The fourth blueprint shows you how to use deep learning to find your business logo in social media photos and assess how people interact with your products. Learn the practical techniques involved and how to apply these blueprints intelligently. The fifth blueprint is about how to best design a ‘trending now’ section on your website, much like the one we know from Twitter. The sixth blueprint shows how to create helpful chatbots so that an AI system can understand customers’ questions and answer them with relevant responses. This book continuously demonstrates a working framework and strategy for building AI business applications. Along the way, you’ll also learn how to prepare for future advances in AI. You’ll gain a workflow and a toolbox of patterns and techniques so that you can create your own smart code.
Table of Contents (14 chapters)
AI Blueprints
Foreword
Contributors
Preface
Other Books You May Enjoy
Index

Deployment strategy


In this project, we will develop a live sentiment detector using articles and comments about autonomous vehicles gathered from traditional online news sources as well as Twitter and Reddit. Aggregate sentiment across these sources will be shown in a plot. For simplicity, we will not connect the sentiment detector to any kind of automated alerting or response system. However, one may wish to review techniques for detecting anomalies, that is, sudden changes in sentiment, as developed in Chapter 6, A Blueprint for Discovering Trends and Recognizing Anomalies.

We will use Java for the backend of this project and Python for the frontend. The backend will consist of the data aggregator and sentiment detector, and the frontend will host the live plot. We choose Java for the backend due to the availability of libraries for sentiment analysis (CoreNLP) and the various APIs we wish to access. Since the frontend does not need to perform sentiment analysis or API access, we are free...