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Architecting AI Solutions on Salesforce

You're reading from  Architecting AI Solutions on Salesforce

Product type Book
Published in Nov 2021
Publisher Packt
ISBN-13 9781801076012
Pages 340 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Lars Malmqvist Lars Malmqvist
Profile icon Lars Malmqvist

Table of Contents (17) Chapters

Preface 1. Section 1: Salesforce and AI
2. Chapter 1: AI Solutions on the Salesforce Einstein Platform 3. Section 2: Out-of-the-Box AI Features for Salesforce
4. Chapter 2: Salesforce AI for Sales 5. Chapter 3: Salesforce AI for Service 6. Chapter 4: Salesforce AI for Marketing and Commerce 7. Chapter 5: Salesforce AI for Industry Clouds 8. Section 3: Extending and Building AI Features
9. Chapter 6: Declarative Customization Options 10. Chapter 7: Building AI Features with Einstein Platform Services 11. Chapter 8: Integrating Third-Party AI Services 12. Section 4: Making the Right Decision
13. Chapter 9: A Salesforce AI Decision Guide 14. Chapter 10: Conclusion 15. Assessments 16. Other Books You May Enjoy

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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