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Sunday, November 13 • 11:10am - 11:30am
Meta Data Science: When all the world's data scientists are just not enough

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Due to privacy concerns and the nature of SAAS businesses, platforms like CRM systems often have to provide intelligent data-driven features that are built from many different unique, per-customer machine learnt models. In the case of Salesforce, this entails building hundreds of thousands of models tuned for as many distinctly different customers for any given data-driven application. In this talk I will describe our home grown scala and SparkML-based machine learning platform that has the following characteristics: - Automated feature engineering resulting in much quicker modeling turnarounds and higher accuracy than general purpose modeling libraries such as scikit-learn. - Automatic hyper-parameter optimization, feature selection and model selection resulting in a very good model for every specific customer of the product. - Modular workflows and transformations that complement systems like SparkML and KeystoneML. - Huge scale that enables training thousands of model per day. This talk will give the audience a good idea of which parts of the typical machine learning pipeline are easier to automate, and which are harder.


Shubha Nabar

Director of Data Science, Salesforce

Sunday November 13, 2016 11:10am - 11:30am
Off by One

Attendees (24)