The SAS Data Science Blog
Advanced analytics from SAS data scientists
When using LLMs, managing toxicity, bias, and bad actors is critical for trustworthy outcomes. Let’s explore what organizations should be thinking about when addressing these important areas.
Learn how an intern integrated SAS Viya® and open-source code (Python) into a Machine Learning project to combine their strengths within the context of predictive modeling, and to show off the variety of ways this integration can be accomplished.
Where GPT-4o is concerned for computer vision, SAS' Jonny McElhinney, Julia Florou-Moreno, and Priti Upadhyay advocate a trust-but-verify approach.
A recent article came out with an updated list of necessary components for MLOps and LLMOps. And while this list may seem long, reading through the capabilities and components, I realized that SAS Viya already covers most of the required functionality. Organizations can have a hodgepodge of tools that they
Adding linguistic techniques in SAS NLP with LLMs not only help address quality issues in text data, but since they can incorporate subject matter expertise, they give organizations a tremendous amount of control over their corpora.
SAS' Greg Massey describes a real-world example of digital transformation for a large customer grappling with manually reviewing patient medical records.