Successfully Build and Scale AI Organizations Beyond the MVP
Successfully Build and Scale AI Organizations Beyond the MVP


The last decade saw a massive revolution in the use of data, with companies effectively leveraging it to transform their business and industries as a whole. But a fully streamlined process for consistently and easily building and deploying new models is rare. Why?

Early in my career, I worked in dozens of organizations, building models and teams to drive transformation, getting to the first MVPs: the minimum viable product, built on a minimum viable platform, using the the minimum viable (and most valuable) players - a small team of skilled individuals. It was challenging, and getting beyond MVP seemed, well, unviable.

The nascent field of AI in industry to drive transformation requires both long-term investment, and consistent learning for how to leverage not just the data, but also the highly specialized individuals and platforms to deliver AI models consistently - not just once.

At Salesforce, we focus on solving this problem, not just for ourselves, but for our 150,000+ customers. To successfully leverage the talented data scientist, we provide them with tools, training and techniques that make it possible to build the automated machine learning that let's our customers build their own models seamlessly.

In this talk, I will take you on the journey of how to build out a successful organization, and how to provide your data scientists with those same tools, techniques and training for which they crave, even if they don't realize it today.


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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