Book Image

Salesforce Lightning Platform Enterprise Architecture - Third Edition

By : Andrew Fawcett
Book Image

Salesforce Lightning Platform Enterprise Architecture - Third Edition

By: Andrew Fawcett

Overview of this book

Salesforce Lightning provides a secure and scalable platform to build, deploy, customize, and upgrade applications. This book will take you through the architecture of building an application on the Lightning platform to help you understand its features and best practices, and ensure that your app keeps up with your customers’ increasing needs as well as the innovations on the platform. This book guides you in working with the popular aPaaS offering from Salesforce, the Lightning Platform. You’ll see how to build and ship enterprise-grade apps that not only leverage the platform's many productivity features, but also prepare your app to harness its extensibility and customization capabilities. You'll even get to grips with advanced application architectural design patterns such as Separation of Concerns, Unit Testing and Dependency Integration. You will learn to use Apex and JavaScript with Lightning Web Components, Platform Events, among others, with the help of a sample app illustrating patterns that will ensure your own applications endure and evolve with the platform. Finally, you will become familiar with using Salesforce DX to develop, publish, and monitor a sample app and experience standard application life cycle processes along with tools such as Jenkins to implement CI/CD. By the end of this book, you will have learned how to develop effective business apps and be ready to explore innovative ways to meet customer demands.
Table of Contents (17 chapters)

Understanding Einstein Platform Services

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 services, and at least 1,000 images per label for Einstein Vision services (for example, 1,000 images of dogs). Once again, the quality of the data you provide directly affects the accuracy of the predictions these APIs provide to your application. 
  • Create...