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Vishakha Gupta-Cledat

Co-Founder & CEO, ApertureData

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.

Vishakha Gupta-Cledat

Sessions / 2022 / 1 talk

  • Just Because Your Data Is Unstructured Doesn’t Mean it Should be Onerous

    Visual data (images/videos) is rich in valuable insights. Data science and ML techniques can help understand visual content and enable better customer experience across application domains, in turn driving its exponential growth. A key factor for success in visual ML is to get the foundational data infrastructure right, which can be extremely challenging and time consuming due to a lack of data management solutions designed with visual data or data science in mind. In this talk, I will start by briefly highlighting why visual data today needs special treatment and how this can be achieved. I will also dive deeper into certain architecture and design decisions that have worked well so far as we build such a database ourselves, and give you a quick preview of our product, ApertureDB, and where we are going next.

Sessions / 2020 / 1 talk

  • Time to Rethink Visual Data Management for Machine Learning

    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.

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