AI for Visual Data: Computer Vision in Business
Key Takeaways:- Learn about high value use cases for image and video data.
- Learn best practices for analyzing and managing visual data.
- Learn about cutting edge innovations in computer vision.
Description
Visual data is becoming increasingly important for business as AI allows you to extract more value from it. However, working with this type of data has a lot of challenges, and mistakes can be expensive. In this webinar, three computer vision executives discuss the best ways to get value from your visual data.
Throughout this session, you’ll learn about high value use-cases for image & video data, best practices for managing and analyzing visual data, and an overview of the latest cutting edge innovations in computer vision.
Presenter Bio
Sanjay Pichaiah is the VP of GTM and Product of Akridata, an AI platform for visual data. Sanjay has held various responsibilities from early concept validation, product definition, finance and operations, and GTM efforts. Prior to Akridata, Sanjay served as the VP of Products and COO of Ampool, Co-Founder and CEO of Trupeco.
Yonatan is the co-founder and CEO of Deci, the deep learning company building the next generation of AI. He co-founded Deci after completing his PhD in computer science at the Technion-Israel Institute of Technology, where his research focused on making Deep Neural Networks (DNNs) more applicable for mission-critical tasks. Yonatan was also a member of Google AI’s MorphNet team where he worked on developing an automated machine learning algorithm based on neural network pruning. His research has been published and presented at leading conferences across the globe.
Vishakha 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.