ML developers and data scientists are increasingly tasked with extracting value from torrents of visual data (images, videos, etc). However, handling big-visual-data requires expertise in large scale infrastructure, and data management solutions were not developed with ML workflows in mind. What current makeshift solutions fail to address is that as ML gets commercialized, managing the onslaught of real visual data is going to be a killer for live deployments. I will talk about what the current infrastructure typically looks like, why the status quo needs to be challenged, and discuss some real world use cases.

Vishakha Gupta-Cledat is Co-founder and CEO of ApertureData. Prior to that, she worked at Intel Labs for over 7 years where she led the design and development of VDMS (the Visual Data Management System) which forms the core of ApertureData’s product, ApertureDB. Vishakha holds a Ph.D in Computer Science from the Georgia Institute of Technology and a M.S. in Information Networking from Carnegie Mellon University. She has worked on scheduling in heterogeneous multi-core environments, graph based storage and applications on non volatile memory systems, and visual data management challenges for analytics use cases. She loves to work on systems which impose stringent requirements in terms of software design and coding and call for innovative solutions. She has served on the program and steering committees of several premier systems conferences.