Sophia Rowland is a Senior Product Manager focusing on MLOps at SAS. In her previous role as a data scientist, Sophia worked with dozens of organizations to solve a variety of problems using analytics. As an active speaker and writer, Sophia has spoken at events like All Things Open, SAS Explore, and SAS Innovate as well as written dozens of blogs and articles. As a staunch North Carolinian, Sophia holds degrees from both UNC-Chapel Hill and Duke including bachelor’s degrees in computer science and psychology and a Master of Science in Quantitative Management: Business Analytics from the Fuqua School of Business. Outside of work, Sophia enjoys reading an eclectic assortment of books, hiking throughout North Carolina, and staying upright while ice skating.
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Word embeddings are the learned representations of words within a set of documents. Each word or term is represented as a real-valued vector within a vector space. Terms or words that reside closer to each other within that vector space are expected to share similar meanings. Thus, embeddings try to capture the meaning of each word or term through its relationships with the other words in the corpus.
SAS System Engineer Sophia Rowland reveals how to embed decision flows into webpages and applications using the Microsoft Power Platform for a better customer experience.
