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.
Want the flexibility of modern Python development with the power of SAS behind it? Explore how SAS Viya Workbench combines familiar tools, on-demand compute, and Python-native access to advanced SAS algorithms in a single browser-based environment.
In my first article on hyperparameter autotuning, I used a cake analogy to show how to use hyperparameter autotuning with Optuna and the sasviya.ml package in Python to improve detecting Higgs bosons in a particle accelerator. SAS Viya Workbench now supports hyperparameter autotuning in SAS code with a variety of
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.
