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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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Toc

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

PredictionIO DASE components and customization of Engine

The engine code base is based on the DASE model. DASE is abbreviated as follows:

D: Data Source and Data Preparator

A: Algorithm

S: Serving

E: Evaluation Metric

The following table describes the function of each of the components and the associated Java classes for the Product Recommendation Engine:

...
Component Functionality Java Class
Data Source and Data Preparator The data Source reads data from the input source and transforms into the format used by the Preparator. The Data Preparator preprocesses the data and forwards it to the algorithm for model training.

DataSource.java

DataSourceParams.java

Preparator.java

Algorithm The Algorithm component includes the Machine Learning algorithm and the settings of its parameters, and determines how a predictive model is constructed.

Algorithm.java

AlgorithmParams.java

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