AI/ML-driven data applications are often considered as the final data application -- the target of all data preparation and metadata management activities performed upfront. However, AI/ML can also effectively aid the data aggregation and categorization for search, analytics, and discovery. Data tagging and metadata management can be automated and thereby, enhance the overall data experience for all entities involved in the data process – business or technical. In this session, we'll cover certain examples of AI/ML in data engineering.
About the Speaker
Dr. Pragyansmita Nayak is the Chief Data Scientist at Hitachi Vantara Federal (HVF). She explores the "Art to the Science" engaging with several challenging Data Engineering and Data Science projects in the US Federal Government domain. She has over 22+ years of experience in software development and data science. She holds a Ph.D. in Computational Sciences and Informatics from GMU (Fairfax, VA. She has presented as part of various BrightTalk and other similar technical events in the past. She lives in DC Metro with her husband and enjoys cooking, travel, and photography. For more information on Pragyan's professional experience, please visit her LinkedIn profile at https://www.linkedin.com/in/pragyansmita and Twitter profile at https://twitter.com/SorishaPragyan.