In the customer age, being able to extract relevant communications information in real time and cross-reference it with context is key. Alexis Roos and Noah Burbank explain how Salesforce uses data science and engineering to enable salespeople to monitor their emails in real time to surface insights and recommendations using a graph modeling contextual data. Alexis and Noah offer an overview of Salesforce AI Inbox’s activity graph (based on emails, meetings, etc.) and detail how Salesforce uses it to offer services such as closest connections and provide context for real-time insights and recommended actions. Along the way, they cover use cases, technical architecture, and best practices as well as an interactive demo and associated code using notebooks running Spark and Scala.
Alexis Roos is director of data science and machine learning at Salesforce, where he leads a team of data engineers and scientists focusing on deriving intelligence from activity data for the Einstein platform. Alexis has over 20 years of software engineering experience, with the last six years focused on large-scale data science and engineering using technologies including data engineering, entity resolution, distributed graph processing, machine learning, natural language processing, and deep learning. He has worked for SIs in Europe, Sun Microsystems/Oracle, and several startups, including Radius Intelligence, Concurrent, and Couchbase. Alexis started learning programming as a teenager and was an avid 68000 programmer. He is a frequent speaker at meetups and conferences such as Spark summit SF and East, Scala by the Bay, Hadoop Summit, O’Reilly Web 2.0, and Java One. He has also led trainings and two university-level courses on big data. Alexis is a mentor at thecamp. He holds a master’s degree in CS with a focus on cognitive sciences.
Noah Burbank is a software engineer on Salesforce’s intelligence services team, where he focuses on the application of artificial intelligence to improve the quality of decisions that his customers can make everyday in their businesses. He holds a PhD in decision and risk analysis from Stanford University, where his research simplified complex decision making techniques for application in everyday life.
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