Modernization lessons from Posten Bring and Regeneron
The best migrations don’t just move workloads. They move organizations forward. Migration is often described in technical terms: workloads moved, systems retired, data transferred, users
Building a governed banking workflow with SAS® Viya® MCP Server and Claude Cowork
Many discussions about Model Context Protocol (MCP) focus on an AI assistant calling an API and returning a result. Banking requires more than pass-through connectivity:
Nonnegative matrix factorization with sparseness constraints
A previous article describes the nonnegative matrix factorization (NMF). You can use the NMF to reveal important features in data that can
Remembering Lex Jansen: Friend to SAS learners everywhere
For many SAS users, lexjansen.com was simply a destination. It was the place you went when you needed to find that paper you vaguely remembered
Cómo anticipar el burnout y construir organizaciones públicas más saludables
El bienestar laboral se ha consolidado como una prioridad estratégica para las organizaciones, tanto públicas como privadas. La Organización Mundial de la Salud define el
How to build predictive health care AI from data to decision
A hospital discharge summary tells you what happened. A predictive model tells you what is about to happen. That distinction is the difference between reactive
Gut housekeeping: how to support your migrating motor complex
Most of us think about digestion only shortly after we eat or if we experience gastrointestinal symptoms, but our guts are busy clearing leftover food
A route to uncover Alzheimer’s two years earlier: How SAS Viya is helping clinicians and patients with AI
The question in AI has shifted from “can we build a model?” to “can we trust it, govern it and put it to work?” DementAI
From SAS 9 to SAS Viya: Modernize analytics without starting over
For many organizations, SAS 9 is more than just an analytics platform. It is an integral part of how critical decisions get made every day,
Kernel density estimates for positive data
The kernel density estimate (KDE) is a powerful tool for estimating the density of univariate data. The KDE is a flexible model. For example,
