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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

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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