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Salesforce Platform Enterprise Architecture- fourth edition

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

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Product type Paperback
Published in Mar 2023
Publisher Packt
ISBN-13 9781804619773
Length 712 pages
Edition 4th Edition
Languages
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Author (1):
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Andrew Fawcett Andrew Fawcett
Author Profile Icon Andrew Fawcett
Andrew Fawcett
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Toc

Table of Contents (23) Chapters Close

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...
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