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6 Applying AI to Existing Platforms

This chapter covers:

  • Integration Patterns for Serverless AI
  • Improving identity verification with Textract
  • An AI Enabled data processing pipeline with Kinesis
  • On the fly translation with Translate
  • Sentiment analysis with Comprehend
  • Training a Custom Document Classifier with Comprehend

In chapters 2, 3 and 4 we created systems from scratch and applied AI services from the start. Of course the real world is not always this clean and simple. Almost all of us have to deal with legacy systems and technical debt. In this chapter we are going to examine some strategies for applying AI services to existing systems. We will start by looking at some architectural patterns for this and from there we will develop some specific examples drawn from real world experience.

6.1  Integration Patterns for Serverless AI

There is no escaping the fact that real world enterprise computing is 'messy'. For a medium to large enterprise the technology estate is typically large, sprawling and has often grown organically over time.

An organization’s compute infrastructure can be broken down along domain lines such as Finance, HR, Marketing, Line of Business systems and so on. Each of these domains may be comprised of many systems from various vendors along with home grown software and will usually mix legacy with more modern software as a service (SaaS)delivered applications.

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

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