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!
Peter Gao is the co-founder and CEO of Aquarium, a YC and Sequoia-backed startup that helps team improve their ML models by improving the data they train on. Previously, Peter was an early employee at Cruise, where he built much of the perception and deep learning systems for a self driving car. Before that, Peter did deep learning research at Berkeley and worked on ML at Khan Academy and Pinterest.