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
Can we use Computer Vision (CV) to recognize the identity of over 500 Galapagos sea turtles by using just an image? This was the question asked of SAS by researchers at the Galapagos Science Center (GSC), a joint partnership between the University of North Carolina at Chapel Hill’s (UNC) Center for
In a previous post, we explored the intricacies of panel data regression. We unveiled a range of panel models and demonstrated their application in estimating cigarette demand by using the CPANEL procedure. However, achieving reliable insights in the realm of panel data regression requires addressing practical challenges. These would include
Panel data are commonly used in today’s economics research. Panel data regression stands out as a powerful tool that aids in unraveling trends and patterns that evolve over time. This tool is particularly valuable when considering hidden factors in the investigations of cause-and-effect relationships. In this post, you will be
Learn how the %FiniteHMM macro can automatically pre-process input data as well as post-process output tables for finite Hidden Markov Models (HMMs) using PROC HMM.
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.
A close look at the first of five DevOps ideals in Gene Kim's book, “The Unicorn Project."
SAS' Phuong Ngo demonstrates an automated shift-left CI/CD security workflow.
Empirical Mode Decomposition (EMD) is a powerful time-frequency analysis technique that allows for the decomposition of a non-stationary and non-linear signal into a series of intrinsic mode functions (IMFs). The method was first introduced by Huang et al. in 1998 and has since been widely used in various fields, such as signal processing, image analysis, and biomedical engineering.
SAS' Ji Shen introduces you to an effective solution for modeling and forecasting count time series.
