Every company needs Artificial Intelligence in production to stay competitive. This is hard both technically but 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.
How does Salesforce manage to make data science an agile partner to over 100,000 customers? We will share the nuts and bolts of the platform and our agile process.
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.
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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