William Nadolski is a Senior Data Scientist in the SAS Applied AI and Modeling division. He previously spent more than seven years as an analytic field consultant working on everything from healthcare fraud to computer vision and text analytic use cases. He is now focused on how AI can accelerate robotic process automation efforts and empower organizations to use their valuable data assets fully.
This post covers the two layers that sit on top of the model and largely determines what an AI system can actually accomplish.
This post familiarizes a practitioner with LLMs, SLMs, and VLMs as well as serving as a field guide for navigating this rapidly evolving terrain.
Small language models like GLiNER provide an efficient, deterministic, and flexible solution for named entity recognition, bridging the gap between traditional NLP and large language models for enterprise information extraction.
