Book Image

Architecting AI Solutions on Salesforce

By : Lars Malmqvist
Book Image

Architecting AI Solutions on Salesforce

By: Lars Malmqvist

Overview of this book

Written for Salesforce architects who want quickly implementable AI solutions for their business challenges, Architecting AI Solutions on Salesforce is a shortcut to understanding Salesforce Einstein’s full capabilities – and using them. To illustrate the full technical benefits of Salesforce’s own AI solutions and components, this book will take you through a case study of a fictional company beginning to adopt AI in its Salesforce ecosystem. As you progress, you'll learn how to configure and extend the out-of-the-box features on various Salesforce clouds, their pros, cons, and limitations. You'll also discover how to extend these features using on- and off-platform choices and how to make the best architectural choices when designing custom solutions. Later, you'll advance to integrating third-party AI services such as the Google Translation API, Microsoft Cognitive Services, and Amazon SageMaker on top of your existing solutions. This isn’t a beginners’ Salesforce book, but a comprehensive overview with practical examples that will also take you through key architectural decisions and trade-offs that may impact the design choices you make. By the end of this book, you'll be able to use Salesforce to design powerful tailor-made solutions for your customers with confidence.
Table of Contents (17 chapters)
Section 1: Salesforce and AI
Section 2: Out-of-the-Box AI Features for Salesforce
Section 3: Extending and Building AI Features
Section 4: Making the Right Decision

Chapter 8: Integrating Third-Party AI Services

This, too, will be a hands-on chapter that takes you through three examples of custom development. In this case, we are using external third-party services as part of normal Sales/Service workflows on Salesforce. The first example will train a custom prediction model to predict the likelihood of a support case resulting in legal liability using Amazon SageMaker, the second will extract key phrases from a case, and the third will bring in automated translations with the Google Translation API. As part of each feature discussion, it will reference the Pickled Plastics Ltd. scenario that is used throughout the book to give a real-world grounding.

In this chapter, we're going to cover the following main topics:

  • Introducing the examples
  • Predicting with a custom model using AWS SageMaker
  • Extracting key phrases with Azure Text Analytics
  • Translating text with Google Translate

After completing this chapter, you...