Andrew is a scientist with advanced degrees in chemistry and toxicology with research, analytics, and data science experience spanning hospital research organizations, pharma, and online education. He is currently an Advanced Analytics Product Engineer at Seattle Childrens, previously holding roles as a content developer (Udacity School of Autonomous Systems), Machine Learning Engineer (Healthslate and Entrée AI), and Analytical Team Leader (Bristol Myers Squibb).
Seattle Children's is dedicated to providing the best medical care possible through strategies which include researchers and clinicians working alongside each other to improve our understanding of pediatric diseases. Full realization of this relationship requires systems and processes designed to enable the capture, discovery, and effective communication of knowledge and information. So how do we enable the translation of knowledge and expertise, generated by our scientists and clinicians, to improve patient care?
In this talk we will discuss how we are building a loosely coupled framework comprised of MLflow, Vega-lite, and other open source tools as part of our knowledge capture, management, and communication strategy. We will demonstrate how we leverage the MLFlow model registry to capture visualizations in a way that makes them discoverable and shareable to clinicians.