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.
ApertureData Platform recognizes the unique characteristics of visual data as well as the importance of its associated metadata, streamlining access and extraction operations across large visual data for scalable ML deployments. Our simple ML-aware interface cuts platform engineering time by months.
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 real deployments. Our talk will explain why status quo needs to be challenged, how ApertureData Platform achieves the performance and functionality important for a wide range of visual ML driven application domains, and demonstrate 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.
Luis Remis, co-founder and CTO, has a Masters in Computer Science from the University of Illinois at Urbana-Champaign, where he did research on scheduling graph algorithms using heterogeneous compute platforms. He worked at Intel Labs for over three years, where he architected, designed, and developed the Visual Data Management System (VDMS), shaping the interface and adding a focus on ML and graphics. He also implemented novel methods for feature vector storage, resulting in the first persistent feature vector index implementation. Prior to that, he was a part of a modeling engineering team at INVAP SE (Argentina), where he focused on research and development on high-performance signal processing for radar systems using accelerators.