DataOps, MLOps, Computer Vision

Implementing state-of-the-art architectures, tuning model hyperparameters, and optimizing loss functions are the fun parts of computer vision. Sexy 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 three 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.
Who is this presentation for?
Data Engineers, Machine Learning Engineers, Machine Learning Product Managers, Head of AI
Prerequisite knowledge:
1 - Understand the Machine Learning lifecycle development 2 - Know about the difference between academic ML and production ML 3 - Exposure to the MLOps tooling ecosystem
What you'll learn?
1 - DataOps for computer vision 2 - Data challenges with computer vision in production 3 - The ideal DataOps lifecycle for a computer vision stack

James Le currently runs Data Relations at Superb AI, a Series A ML data management startup. As part of his role, James executes content and partnership initiatives - while working cross-functionally with growth, product, customer success, sales, marketing, and community functions to drive Go-To-Market strategy. Before joining Superb AI, he completed his Computer Science Master's degree at RIT, where his research thesis lies at the intersection of deep learning and recommendation systems. Outside of work, he is highly active in the broader data and ML community - writing data-centric blog posts, hosting a data-focused podcast, and organizing in-person community events.