Co-Founder and CEO, Aquarium Learning
Peter is the cofounder and CEO of Aquarium, an ML data management system that helps teams improve their model performance by improving their datasets. Aquarium helps users find and fix common errors like labeling errors and patterns of model failures. Aquarium’s customers include companies across industries like logistics, industrial inspection, agriculture, waste management, and more. Before Aquarium, Peter was an early employee at Cruise Automation, where he helped build the deep learning stack for a fleet of self driving cars and saw first-hand the difficulties of making models work well in production. Earlier, Peter worked on ML teams at Khan Academy and Pinterest.
Watch live: March 2 | 2:30PM ET
It’s The Data, Stupid! How Improving ML Datasets Is The Best Way To Improve Model Performance
When working to improve an ML model, many teams will immediately turn to fancy models or hyperparameter tuning to eke out small performance gains. However, the majority of model improvement can come from holding the model code fixed and properly curating the data it’s trained on! In this talk, Peter discusses why data curation is a key part of model iteration, some common data and model problems, then discusses how to build workflows + team structures to efficiently identify and fix these problems in order to improve your model performance.