Let SAS handle the data prep, R take care of the modeling, and skip the environment-hopping so your team can focus on building cool stuff faster.
Learn how to modernize legacy SAS workflows by integrating Python and automating processes using GitHub Actions and SAS Viya Workbench, enabling seamless collaboration and CI/CD across development environments.
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.
A hands-on introduction to SAS Notebooks in SAS Viya Workbench, showing how to combine code, SQL, and Markdown for a more productive and interactive coding experience.
SAS' Enhanced Support model is structured into five progressive levels, ranging from Limited Support—designed for outdated software versions—to Premium Support, which offers the highest service standards, including enhanced SLAs, 24/7 assistance, and direct access to product experts. In particular, the combination of Extended Technical Support and Premium Support has proven to be a winning strategy for many companies undergoing modernization. This approach allows continued support for legacy environments during the migration to newer versions, minimizing operational risks and ensuring service continuity.
Using SAS Viya Workbench for efficient setup and execution, this beginner-friendly guide shows how Scikit-learn pipelines can streamline machine learning workflows and prevent common errors.
This guide shares the top keyboard shortcuts to boost productivity in VSCode, especially when using SAS Viya Workbench with Python or SAS notebooks. It includes built-in commands for navigating and editing notebooks, plus instructions for creating custom shortcuts tailored to your coding workflow.
Using 47 seasons of Survivor data, this analysis explores what gameplay traits correlate with winning, applying Python and SAS Viya Workbench to build predictive models. While stats like challenge wins and voting accuracy help narrow down potential winners, the findings suggest that intangible social dynamics play the most decisive role.
Learn about how I used Python, SAS, GPS, and heart rate data to track and visualize my snowboarding performance.
Generative adversarial networks (GANs) offer a promising solution by creating synthetic data that mimics real datasets, allowing developers to build models without exposing sensitive customer information.
