Bernease Herman
Bernease Herman is a data scientist at WhyLabs and a research scientist at the University of Washington eScience Institute. At WhyLabs, she is building model and data monitoring solutions using approximate statistics techniques. Her academic research focuses on evaluation metrics and interpretable ML with specialty on synthetic data and societal implications. She is a PhD student at the University of Washington and holds a Bachelor’s degree in mathematics and statistics from the University of Michigan.
Sessions
Monitoring data science and AI applications is different in the era of generative AI, large language and vision models (LLVMs), and embeddings, especially given the massive datasets involved. We discuss how to monitor this increasingly common data in a truly scalable way using open source data logging library, whylogs.