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Learning Salesforce Einstein

You're reading from   Learning Salesforce Einstein Add artificial intelligence capabilities to your business solutions with Heroku, PredictiveIO, and Force

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787126893
Length 334 pages
Edition 1st Edition
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Author (1):
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Mohit Shrivatsava Mohit Shrivatsava
Author Profile Icon Mohit Shrivatsava
Mohit Shrivatsava
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Table of Contents (10) Chapters Close

Preface 1. Introduction to AI FREE CHAPTER 2. Role of AI in CRM and Cloud Applications 3. Building Smarter Apps Using PredictionIO and Heroku 4. Product Recommendation Application using PredicitionIO and Salesforce App Cloud 5. Salesforce Einstein Vision 6. Building Applications Using Einstein Vision and Salesforce Force.com Platform 7. Einstein for Analytics Cloud 8. Einstein and Salesforce IoT Cloud Platform 9. Measuring and Testing the Accuracy of Einstein

Getting started with PredictionIO

In this section, we will explore how to build and deploy an engine using the templates offered by PredictionIO.

To start with, let's pick up an e-commerce Recommendation Template to start building the Engine. We will add code snippets for both Java and Scala. If you are familiar with one of these languages, it will help you understand the supporting content.

PredictionIO, for its core engine implementation, uses Scala, even though there is no hard rule to use Scala to build on top of PredictionIO. You can use Java to code the engine and the algorithm as well. Spark MLlib also supports both Java and Scala.

The URL for the template for the Java version that we will use is located at https://github.com/apache/incubator-predictionio-template-java-ecom-recommender.

The Scala version of the template engine that we will use is located at https:...

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