Director of Einstein
Whether automating digital customer interactions using bots, predicting the best sales and marketing targets, or reducing waste in logistics and manufacturing - Artificial Intelligence improves business operations once deployed. Companies need to redefine themselves by building models and learning from data.
Companies often start with building single models, and quickly realize that this is only the first step. Making predictions available at the right time, in the right context to drive the action requires significant effort: connecting data streams, extracting features, training models and sending predictions to a front-end application. Beyond all the engineering, it means infrastructure and alerting need to be in place, along with an ability to experiment and iterate. Once companies deploy one such model, replicating this success is even more difficult. Generalizing the methods and avoiding duplicated effort is necessary if the desire is to go beyond a handful of additional models. Even so, without planning this means taking one-off approaches to painstakingly handle increased data volumes and variety, new modeling approaches, different applications, etc. Scaling to 100s becomes improbable.
At Salesforce we need to surpass hundreds of thousands. For this we built the Einstein Platform. With its automation of Artificial Intelligence and services built for handling 1000s of customers, each with multiple models. From data ingestion, automated machine learning, experimentation frameworks, and instrumentation and intelligent monitoring and alerting make it possible to serve the varied needs of many different businesses. In this talk we will cover the nuts and bolts of the system, and share how we learned to solve for scale and variability with a fully operational Machine Learning platform
议题介绍：Sarah Aerni is a Director 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.