Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
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Automated machine learning for Agile data science at scale

Sarah Aerni (Salesforce)
11:50am12:30pm Wednesday, March 27, 2019
Average rating: ****.
(4.25, 4 ratings)

Who is this presentation for?

  • Data scientists, machine learning engineers, product managers, data science managers, and engineers transitioning to data science



Prerequisite knowledge

  • A basic understanding of machine learning and Agile methodologies

What you'll learn

  • Learn how Salesforce enables data scientists to rapidly iterate and adopt a truly Agile methodology


Every company needs artificial intelligence in production to stay competitive. This is hard technically and also from a process standpoint. Many engineering organizations struggle to provide their data scientists with the tools needed to effectively deploy models into production and continuously iterate. So how does Salesforce make data science an Agile partner to over 100,000 customers?

Sarah Aerni shares the nuts and bolts of the company’s platform and details the Agile process behind it. From open source autoML library TransmogrifAI and experimentation to deployment and monitoring, Sarah covers the tools that make it possible for data scientists to rapidly iterate and adopt a truly Agile methodology.

TransmogrifAI makes it easy for Salesforce’s data scientists to contribute new ways of solving challenging problems and evaluating them at scale using experimentation frameworks. The platform helps them ship the code to production to all customers simultaneously, automating the process of retraining thousands of models and shipping billions of predictions per day. And with modeling comes the need to detect issues and identify opportunities for improvements. Sarah explains how Salesforce uses alerting and monitoring to keep track of the individual models that its 100,000+ customers can build in a completely automated way and drive the company’s data science backlog. Along the way, Sarah discusses lessons learned about rapid iteration and ensuring data science innovation continues according to a truly Agile methodology.

Photo of Sarah Aerni

Sarah Aerni


Sarah Aerni is a director of data science at Salesforce Einstein, where she works to democratize machine learning by building products that allow customers to deploy predictive apps in a few short clicks. Her team builds automated machine learning pipelines and products like Einstein Prediction Builder. Previously, she led teams in healthcare and life sciences at Pivotal building models for customers. She is a recovering academic and entrepreneur, with a PhD in biomedical informatics from Stanford University and a passion for building diverse teams, education, and exploration.

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Picture of Russell Jurney
04/01/2019 9:05am PDT

This was great, thanks so much for presenting this, Sarah!