Stu is a Senior Product Manager for Open Source Data Science at SAS with a vision to advance high performance composite AI that is accessible by data scientists of all programming backgrounds. He earned a BS in Statistics in 2012 from North Carolina State University and an MS in Advanced Analytics from the Institute for Advanced Analytics in 2013. In previous roles, Stu served as Product Manager for augmented analytics and as a Data Scientist specializing in forecasting, statistical analysis, and business intelligence. Stu champions industry-leading analytics experiences, empowering data scientists to make intelligent, data-driven decisions that shape their organizations' futures.
Learn how to seamlessly register and deploy Python models (specifically an XGBoost classifier) into SAS Model Manager using SAS Viya Workbench and the pzmm package, enabling efficient ModelOps integration and production readiness.
Hyperparameter autotuning intelligently optimizes machine learning model performance by automatically testing parameter combinations, balancing accuracy and generalizability, as demonstrated in a real-world particle physics use case.
Learn about how I used Python, SAS, GPS, and heart rate data to track and visualize my snowboarding performance.
Wouldn’t it be great if we could create a Python environment with only the packages and versions we need? Enter: virtual environments.
Generative AI (GenAI) is here to stay – there’s no question about it. A recent SAS survey of 1,600 organizations found that 54% have begun implementing It, and 86% plan to invest in it within the next financial year. As organizations integrate AI into their workflows, a critical question arises:
