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Summary
Companies everywhere are moving everyday business processes over to the cloud, and AI is increasingly being given the reins in these tasks. As this massive digital transformation continues, the combination of serverless computing and AI promises to become the de facto standard for business-to-consumer platform development--and developers who can design, develop, implement, and maintain these systems will be in high demand AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory. Instead, you'll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Cloud-based AI services can automate a variety of labor intensive business tasks in areas such as customer service, data analysis, and financial reporting. The secret is taking advantage of pre-built tools like Amazon Rekognition for image analysis or AWS Comprehend for natural language processing. That way, there's no need to build expensive custom software. Artificial Intelligence (AI), a machine's ability to learn and make predictions based on patterns it identifies, is already being leveraged by businesses around the world in areas like targeted product recommendations, financial forecasting and resource planning, customer service chatbots, healthcare diagnostics, data security, and more.

With the exciting combination of serverless computing and AI, software developers now have enormous power to improve their businesses' existing systems and rapidly deploy new AI-enabled platforms. And to get on this fast-moving train, you don't have to invest loads of time and effort in becoming a data scientist or AI expert, thanks to cloud platforms and the readily available off-the-shelf cloud-based AI services

About the book
AI as a Service is a fast-paced guide to harnessing the power of cloud-based solutions. You'll learn to build real-world apps--such as chatbots and text-to-speech services--by stitching together cloud components. Work your way from small projects to large data-intensive applications.

What's inside

- Apply cloud AI services to existing platforms
- Design and build scalable data pipelines
- Debug and troubleshoot AI services
- Start fast with serverless templates

About the reader
For software developers familiar with cloud basics.

About the author
Peter Elger and E in Shanaghy are founders and CEO/CTO of fourTheorem, a software solutions company providing expertise on architecture, DevOps, and machine learning.

Table of Contents

PART 1 - FIRST STEPS

1 A tale of two technologies

2 Building a serverless image recognition system, part 1

3 Building a serverless image recognition system, part 2

PART 2 - TOOLS OF THE TRADE

4 Building and securing a web application the serverless way

5 Adding AI interfaces to a web application

6 How to be effective with AI as a Service

7 Applying AI to existing platforms

PART 3 - BRINGING IT ALL TOGETHER

8 Gathering data at scale for real-world AI

9 Extracting value from large data sets with AI

Peter Elger is the founder and CEO of fourTheorem, a software solutions company providing expertise on architecture, development, DevOps, and machine learning. He is also the cofounder and CTO of two other companies: nearForm, a Node.js consultancy, and StitcherAds, a digital advertising platform. He is a regular conference speaker and coauthor of The Node Cookbook as well as several academic papers.

Eoin Shanaghy is the cofounder and CTO fourTheorem and the founder of Showpiper, a machine learning-driven video content marketing startup. He previously created real-time trading applications for Fortis Bank and developed Hewlett-Packard's enterprise application server as well as 3G network management systems for Ericsson and Arantech (now Netscout).