AI as a Service
Peter Elger, Eóin Shanaghy
  • MEAP began March 2019
  • Publication in November 2019 (estimated)
  • ISBN 9781617296154
  • 325 pages (estimated)
  • printed in black & white

Illustrates the full potential of AWS.

Maria Geminim
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!
Table of Contents detailed table of contents

Part 1: First steps

1 A Tale of Two Technologies

1.1 Cloud Landscape

1.2 What is Serverless?

1.3 The Need for Speed

1.3.1 The Early Days

1.3.2 The Unix Philosophy

1.3.3 Object Orientation and Patterns

1.3.4 Java, J2EE, .NET,

1.3.5 XML and SOA

1.3.6 Web Speed

1.3.7 Cloud Computing

1.3.8 Microservices (Re-Discovery)

1.3.9 Cloud Native Services

1.3.10 The Trend: Speed

1.4 What is AI?

1.4.1 Real World AI

1.4.2 AI Services

1.4.3 AI and Machine Learning

1.4.4 Deep Learning

1.4.5 AI Challenges

1.5 The Democratization of Compute Power and Artificial Intelligence

1.6 Canonical Serverless AI architecture

1.6.1 Web Application

1.6.2 Realtime Services

1.6.3 Batch Services

1.6.4 Communication Services

1.6.5 Utility Services

1.6.6 AI Services

1.6.7 Data Services

1.6.8 Operational Support

1.6.9 Development Support

1.6.10 Off Platform

1.7 Realization on Amazon Web Services

1.8 Summary

2 Building a Serverless Image Recognition System

2.1 Our First System

2.2 Architecture

2.2.1 Web Application

2.2.2 Synchronous Services

2.2.3 Asynchronous Services

2.2.4 Communication Services

2.2.5 AI Services

2.2.6 Data Services

2.2.7 Development Support and Operational Support

2.3 Getting ready

2.3.1 Set up a Domain

2.3.2 Set up a Certificate

2.3.3 Setting up your Local Environment

2.3.4 Get the Code

2.3.5 Setting Up Cloud Resources

2.4 Implementing the Asynchronous services

2.4.1 Distributor Service

2.4.2 Crawler Service

2.4.3 Analysis Service

2.5 Implementing the Synchronous Services

2.5.1 UI Service

2.5.2 Front-end Service

2.6 Running the System

2.7 Removing the System

2.8 Summary

Part 2: Tools of the Trade

3 Build and Secure a Web Application the Serverless Way

3.1 The To Do List

3.2 Architecture

3.2.1 Web Application

3.2.2 Synchronous Services

3.2.3 Asynchronous Services

3.2.4 Communication Fabric

3.2.5 Utility Services

3.2.6 AI Services

3.2.7 Data Services

3.2.8 Development Support and Operational Support

3.3 Getting ready

3.3.1 Get the Code

3.4 Step 1 The Basic Application

3.4.1 Resources

3.4.2 To-do Service

3.4.3 Front End

3.4.4 Deploying Step 1

3.5 Step 2 Securing with Cognito

3.5.1 Get the Code

3.5.2 User Service

3.5.3 To-do service

3.5.4 Front-end service

3.5.5 Deploying Step 2

3.6 Summary

4 Adding AI Interfaces to a Web Application

4.1 Step 3 Adding a Speech to Text Interface

4.1.1 Get the Code

4.1.2 Note Service

4.1.3 Front End Updates

4.1.4 Deploy Step 3

4.1.5 Test Step 3

4.2 Step 4 Adding Text to Speech

4.2.1 Get the Code

4.2.2 Schedule Service

4.2.3 Front End Updates

4.2.4 Deploy Step 4

4.2.5 Test Step 4

4.3 Step 5 Adding a Conversational Chat Bot Interface

4.3.1 Get the Code

4.3.2 Create the Bot

4.3.3 Front End Updates

4.3.4 Deploy Step 5

4.3.5 Test Step 5

4.4 Removing the System

4.5 Summary

5 How to be Effective with AI as a Service

6 Applying AI to Existing Platforms

6.1 Integration Patterns for Serverless AI

6.1.1 Pattern 1 Synchronous API

6.1.2 Pattern 2 Asynchronous API

6.1.3 Pattern 3 VPN Stream In

6.1.4 Pattern 4 VPN Fully Connected Streaming

6.1.5 Which Pattern?

6.2 Improving identity verification with Textract

6.2.1 Get the Code

6.2.2 Text Analysis API

6.2.3 Client Code

6.2.4 Deploy the API

6.2.5 Test the API

6.2.6 Remove The API

6.3 An AI Enabled Data Processing Pipeline With Kinesis

6.3.1 Get the Code

6.4 Deploying the API

6.5 On the fly translation with Translate

6.6 Testing the Pipeline

6.7 Sentiment Analysis With Comprehend

6.8 Training a Custom Document Classifier

6.8.1 Create a Training Bucket

6.8.2 Upload Training Data

6.8.3 Create an IAM Role

6.8.4 Run Training

6.9 Using the Custom Classifier

6.10 Testing the Pipeline End to End

6.11 Removing the Pipeline

6.12 Benefit

Part 3: Bringing it all together

7 Gathering data at scale for real-world AI

8 Extracting Value from Large Data Sets with AI

Appendixes

Appendix A: Setting up AWS

A.1 Set up an AWS Account

A.1.1 Providing your login credentials

A.1.2 Providing your payment details

A.1.3 Verifying your identity

A.1.4 Choosing your support plan

A.2 Signing In

A.3 AWS Command Line

A.3.1 Installing the AWS CLI on Linux

A.3.2 Installing the AWS CLI on MacOS

A.3.3 Installing the AWS CLI on other platforms

About the Technology

In serverless computing, servers are replaced by cloud functions that are executed on demand, reducing—or even removing!—the need for managing complex infrastructures. With benefits including reduced operational costs and vendor dependencies, increased focus on functionality versus admin, and ease of management, it’s no wonder serverless computing is becoming the norm in business.

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 teaches you how to quickly harness the power of serverless computing and cloud-based AI services. After an introduction to the basics of this dynamic technology duo, you’ll dive right into your first hands-on serverless AI project: a system that can recognize images from arbitrary web pages. In it you’ll explore tools like Amazon Rekognition for image analysis and techniques like deployment of cloud infrastructure, a crawler service, and a simple API. When you’ve mastered the concepts and skills in that fun and interesting project, you’ll move on to building a serverless to-do application that employs cloud-based AI tools like AWS Transcribe and Polly for speech-to-text and text-to-speech functionality and Lex for creating interactive chatbots. When you’re finished with this essential book, you’ll have the skills to quickly build end-to-end serverless AI systems, making you indispensable as this rapidly emerging paradigm becomes the business standard!

What's inside

  • Cloud AI from development to production
  • Applying cloud AI services to your existing platform
  • Understanding orchestration patterns for cloud AI systems
  • How to architect and build scalable, resilient data pipelines
  • Debugging and troubleshooting cloud AI services
  • Getting started immediately with serverless templates

About the reader

For software developers with intermediate skills in at least one programming language and a basic understanding of IP networking and HTTP protocol. Familiarity with cloud-based version control systems such as GitHub would be helpful. No prior knowledge of AI is necessary.

About the authors

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.

Eóin 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).


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