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DataOps for the Modern Computer Vision Stack


Implementing state-of-the-art architectures, tuning model hyper-parameters, and optimizing loss functions are the fun parts of computer vision. As good as it may seem, behind each model that gets deployed into production are data labelers and data engineers responsible for building a high-quality training dataset that serves as the model’s input. In this talk, I will provide an overview of DataOps for computer vision, outline the key data-related challenges that any computer vision teams have to deal with, and propose specific functions of an ideal DataOps platform to address these challenges.
By the end of session attendees will know:
  1. What is DataOps?
  2. Why DataOps For Computer Vision?
  3. Key Principles of DataOps
  4. DataOps Pipeline for the Computer Vision Stack
  5. Data Challenges for Computer Vision Teams
  6. The Future of the Modern Computer Vision Stack
James Le

Data Advocate at Superb AI

James Le is a Data Advocate at Superb AI, a data management platform for computer vision use cases. Previously, he conducted research that lies at the intersection of deep learning and recommendation systems at RIT. Outside work, he writes data-centric blog posts, hosts a data-focused podcast, and organizes in-person events for the data community.

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