Building a successful ModernAI application often requires large volumes of data for training ML models or data that has been organized into knowledge using taxonomies or ontologies to support specific vertical markets (healthcare, insurance, pharma, etc.) or horizontal functions (HR, legal, supply chain, etc). While tools do exist to help developers ingest and organize the required data into meaningful knowledge stores, using pre-built data or knowledge packages can make application development faster, more reliable, and less expensive than starting from scratch.

In this webinar we will look at trends and examples of specific proprietary and open source data sets that offer prebuilt knowledge, representations, or models to serve these markets.

Adrian Bowles

Founder, STORM Insights, Inc.

Adrian is an industry analyst and recovering academic, providing research and advisory services for buyers, sellers, and investors in emerging technology markets. His coverage areas include cognitive computing, big data / analytics, the Internet of things, and cloud computing. Adrian co-authored Cognitive Computing and Big Data Analytics (Wiley, 2015) and is currently writing a book on the business and societal impact of these emerging technologies. He has held executive positions at several consulting and analyst firms. Adrian also held academic appointments in computer science at Drexel University and SUNY-Binghamton, and adjunct faculty positions in the business schools at NYU and Boston College. He began his career with research and application development roles at IBM and GTE Laboratories. Adrian earned his BA in Psychology and MS in Computer Science from SUNY-Binghamton, and his Ph.D. in Computer Science from Northwestern University.