You're reading from Salesforce Platform Enterprise Architecture- fourth edition A must-read guide to help you architect and deliver packaged applications for enterprise needs

Product type Paperback

Published in Mar 2023

Publisher Packt

ISBN-13 9781804619773

Length 712 pages

Edition 4th Edition

Languages

Java

Tools

Salesforce

Concepts

Solution Architecture

Author (1):

Andrew Fawcett

View More author details

Table of Contents (23) Chapters

Preface

1. Part I: Key Concepts for Application Development

2. Building and Publishing Your Application FREE CHAPTER

3. Leveraging Platform Features

4. Application Storage

5. Apex Execution and Separation of Concerns

6. Part II: Backend Logic Patterns

7. Application Service Layer

8. Application Domain Layer

9. Application Selector Layer

10. Additional Languages, Compute, and Data Services

11. Part III: Developing the Frontend

12. Building User Interfaces

13. User Interfaces and the Lightning Component Framework

14. Part IV: Extending, Scaling, and Testing an Application

15. Providing Integration and Extensibility

16. Asynchronous Processing and Big Data Volumes

17. Unit Testing

18. Source Control and Continuous Integration

19. Integrating with External Services

20. Adding AI with Einstein

21. Other Books You May Enjoy

22. Index

Understanding Einstein Platform Services

AI is at its most powerful when coupled with a specific need and outcome; the same thinking applies to the tools described in this chapter and Einstein Platform Services described in this section. Scanning business cards, inspecting products for quality, collecting positive customer stories, and identifying negative reviews are but a few ways in which we see AI being used today. You can access Einstein Platform Services through REST APIs, using either Apex or other languages of your choosing. Regardless of whether you are using these services for image detection or text sentiment analysis, the following high-level process is followed:

Define labels that represent how you want to categorize information, such as dogs, cats, cars, vans, positive, and negative.
Create datasets using the APIs to upload examples for each of the labels you define. You should aim to obtain around 200–500 examples per label for Einstein Language...