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.
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 Viya can allow users and organizations to more easily interface with the LLM application, build better prompts and evaluate systematically which of these prompts leads to the best responses to ensure the best outcomes.
The National Institute of Standards and Technology (NIST) has released a set of standards and best practices within their AI Risk Management Framework for building responsible AI systems. NIST sits under the U.S. Department of Commerce and their mission is to promote innovation and industrial competitiveness. NIST offers a portfolio
SAS Model Manager and the sasctl packages aim to create a seamless ModelOps and MLOps process for Python and R models. Python and R models are not second-class citizens within SAS Model Manager. SAS, Python, and R models can be easily managed using our no-code/low-code interface. This is an interface that can be extended to support a variety of use cases.
How did we get to a place where a conversational chatbot can quickly create a personalized letter? Join us as we explore some of the key innovations over the past 50 years that help inform us about how to respond and what the future might hold.
I will show you how to deploy multi-stage deep learning (DL) models in SAS Event Stream Processing (ESP) and leverage ESP on Edge via Docker containers to identify events of interest.
Using such features and Natural Language Processing capabilities like text parsing and information extraction in SAS Visual Text Analytics (VTA) helps us uncover emerging trends and unlock the value of unstructured text data.
Using SAS Viya in combination with open-source capabilities, we were able to develop an automated solution for logo detection that does not require any manual data labeling.
