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Architecting AI Solutions on Salesforce

Architecting AI Solutions on Salesforce

By : Lars Malmqvist
4.8 (13)
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Architecting AI Solutions on Salesforce

Architecting AI Solutions on Salesforce

4.8 (13)
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)
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1
Section 1: Salesforce and AI
3
Section 2: Out-of-the-Box AI Features for Salesforce
8
Section 3: Extending and Building AI Features
12
Section 4: Making the Right Decision

Predicting with a custom model using AWS SageMaker

Amazon Web Services (AWS) is the biggest player in the cloud marketplace and that includes AI services. In this section, we will be using four different services:

  • AWS SageMaker: A managed service for custom machine learning models
  • S3: The object storage layer of AWS
  • AWS Lambda: Serverless functions running in the cloud
  • API Gateway: The way you expose external APIs on AWS

The purpose of this example is to teach you the basics of using a custom machine learning model from Salesforce. To fit within the available amount of space, we will be skipping several elements that would normally be considered best practice, so don't use this example directly in a production environment. However, you will get an appreciation of how these elements can be formed into a solution should you need to architect one in the future.

As always, we return to our Pickled Plastics Ltd. scenario for our requirements. The legal...

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