Book Image

Learning Salesforce Einstein

By : Mohit Shrivatsava
Book Image

Learning Salesforce Einstein

By: Mohit Shrivatsava

Overview of this book

Dreamforce 16 brought forth the latest addition to the Salesforce platform: an AI tool named Einstein. Einstein promises to provide users of all Salesforce applications with a powerful platform to help them gain deep insights into the data they work on. This book will introduce you to Einstein and help you integrate it into your respective business applications based on the Salesforce platform. We start off with an introduction to AI, then move on to look at how AI can make your CRM and apps smarter. Next, we discuss various out-of-the-box components added to sales, service, marketing, and community clouds from Salesforce to add Artificial Intelligence capabilities. Further on, we teach you how to use Heroku, PredictionIO, and the Force platform, along with Einstein, to build smarter apps. The core chapters focus on developer content and introduce PredictionIO and Salesforce Einstein Vision Services. We explore Einstein Predictive Vision Services, along with analytics cloud, the Einstein Data Discovery product, and IOT core concepts. Throughout the book, we also focus on how Einstein can be integrated into CRM and various clouds such as sales, services, marketing, and communities. By the end of the book, you will be able to embrace and leverage the power of Einstein, incorporating its functions to gain more knowledge. Salesforce developers will be introduced to the world of AI, while data scientists will gain insights into Salesforce’s various cloud offerings and how they can use Einstein’s capabilities and enhance applications.

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Table of Contents (10 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Introduction to AI
Introduction to AI
Artificial Intelligence key terms
Programming languages used for machine learning
Practical machine learning with Google Prediction API and Salesforce
Summary
2
Role of AI in CRM and Cloud Applications
Role of AI in CRM and Cloud Applications
Sales Cloud Einstein offerings
Community Cloud Einstein features
Marketing Cloud Einstein features
Summary
3
Building Smarter Apps Using PredictionIO and Heroku
Building Smarter Apps Using PredictionIO and Heroku
Introduction to PredictionIO
Architecture and integration with applications
Installation of PredictionIO
Getting started with PredictionIO
PredictionIO DASE components and customization of Engine
Summary
4
Product Recommendation Application using PredicitionIO and Salesforce App Cloud
Product Recommendation Application using PredicitionIO and Salesforce App Cloud
Introduction to Spark MLlib
Setting up the Event Server app on Heroku
Setting up the Recommendation engine application on Heroku
Setting up IntelliJ IDEA IDE for customizing PredictionIO application
Introduction to building Lightning Component for App Cloud and Community Cloud
Building similar Recommendation Lightning Component for App Cloud
PredictionIO commands cheat sheet
Summary
5
Salesforce Einstein Vision
Salesforce Einstein Vision
Signing up for Einstein Vision account
Explore Einstein Vision APIs
Set up the Heroku add-on for Einstein Vision Services
Building Node.js application using Einstein Vision on Heroku using React
Testing the application on localhost
Summary
6
Building Applications Using Einstein Vision and Salesforce Force.com Platform
Building Applications Using Einstein Vision and Salesforce Force.com Platform
Set up authorization between Salesforce and Einstein Vision APIs
Creating and training dataset via Apex
Creating an administration app for creating and training dataset
Creating Lightning Components to recognize image
Summary
7
Einstein for Analytics Cloud
Einstein for Analytics Cloud
Setting up Wave Analytics Cloud
Creating datasets, lenses, and dashboards
Using transformations to create dataset
Wave Analytics SAQL, XMD 2.0, and dataset Row-Level Security
Introduction to Einstein Data Discovery
Importing Salesforce data into Einstein Data Discovery and creating stories
Summary
8
Einstein and Salesforce IoT Cloud Platform
Einstein and Salesforce IoT Cloud Platform
IoT Cloud key terms
IoT Cloud components
Apache Kafka on Heroku
IoT integration on the Salesforce Force.com platform
Using PredictionIO on IoT events
Summary
9
Measuring and Testing the Accuracy of Einstein
Measuring and Testing the Accuracy of Einstein
Measuring the accuracy of Sales Cloud Einstein
Building evaluation metrics for the PredictionIO systems
Measuring the accuracy of Salesforce Einstein Vision
Summary

Setting up the Recommendation engine application on Heroku

A similar Product Recommendation Engine that we will use for this uses the PredictionIO template documented at http://predictionio.incubator.apache.org/templates/similarproduct/quickstart. Once the engine template is deployed, we will have a RESTFUL endpoint that can take a predictive query and start recommending the items. We are using the Similar Product Scala template located here (https://github.com/apache/incubator-predictionio-template-similar-product).

The engine uses the Collaborative Filtering discussed in the Introduction To Spark MLlib section and MLlib for the ALS (Alternate Least Square) algorithm. You can find the entire project on the GitHub repository (https://github.com/msrivastav13/pio-engine-heroku). This git repository has been forked from the Salesforce evangelist team and the project is modified to...

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