Presented By

June 18-21, 2019
Beijing, CN

Achieving Salesforce-Scale Machine Learning in Production

This will be presented in English.

Sarah Aerni (Salesforce Einstein)
11:1511:55 Friday, June 21, 2019

必要预备知识 (Prerequisite Knowledge)


您将学到什么 (What you'll learn)

how to set up your organization for data science success, foundational elements required to achieve data science in production

描述 (Description)

In the fourth industrial revolution, every organization must successfully leverage their data to better serve their customers and more effectively run their business. And while machine learning and artificial intelligence were historically viewed as a competitive advantage, they will become an essential part of every business process. They well become necessary. However, the journey to bringing these techniques to production is challenging. Hurdles exist, in technical implementation and talent acquisition.
At Salesforce Einstein we make data science an agile partner to over 100,000 customers. How do we achieve this scale? We share lessons learned in business, technology and process along the way. Via use cases, oft-missed foundational elements that are a prerequisite before any deployment, and the evaluations that must happen along the way, we will share our perspectives on how to measure and achieve success for data science in production, and where to go from there.

The foundational elements of any platform are the ability for data scientists to experiment and rapidly deploy to production. With our open-source autoML library (TransmogrifAI), we make it easy for our data scientists to contribute new ways of solving challenging problems and evaluating them at-scale using experimentation frameworks. Our platform helps them ship the code to production to all customer simultaneously, automating the process of retraining 1000s of models and shipping billions of predictions per day.

With modeling of course comes the need to detect issues and identify opportunities for improvements. We will cover how we use alerting and monitoring to keep track of the individual models that our 100,000+ customers can build in a completely automated way, and drive our data science backlog.

Throughout, we will share lessons learned around rapid iteration and how to ensure data science innovation continues in a truly agile methodology.

Photo of Sarah Aerni

Sarah Aerni

Salesforce Einstein

Sarah Aerni is a Director of Data Science at Salesforce Einstein, where she leads teams building AI-powered applications using autoML. Prior to Salesforce she led the healthcare & life science and Federal teams at Pivotal. Sarah obtained her PhD from Stanford University in Biomedical Informatics, performing research at the interface of biomedicine and machine learning. She also co-founded a company offering expert services in informatics to both academia and industry.

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