Achieving Salesforce-Scale Machine Learning in Production
Achieving Salesforce-Scale Machine Learning in Production


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 focused on making data science an agile partner to over 100,000 customers. How do we achieve this scale? We will share lessons learned in business, technology and process along the way. With a variety of use cases, to the 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.


Sarah Aerni is a Senior Manager of Data Science at Salesforce Einstein, where she leads teams building AI-powered applications across the Salesforce platform. 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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