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Discover how Python developers can work directly in SAS Viya using the SAS Extension for VS Code, combining Python, SAS, SQL, and open data formats in a familiar development experience that is ready for production.
AI copilots are quickly becoming part of how people work with data, analytics and AI. But the real test isn't whether they can generate an answer. It's whether they can help people move faster without sacrificing transparency, oversight or trust. Organizations are under increasing pressure to turn data into decisions
SAS has more than 25 common probability distributions that are supported in the PDF, CDF, QUANTILE, and RAND functions. If you want to work with a less common distribution, you can implement these functions yourself. For example, I previously showed how to use PROC FCMP in Base SAS to implement
AI 에이전트는 개인의 생산성 향상을 넘어 정부 운영 영역에도 그 영향력을 넓혀가고 있습니다. 가트너(Gartner)에 따르면, 2028년까지 최소 80%의 정부가 AI 에이전트를 도입하여 반복적인 의사결정을 자동화하고 효율성과 서비스 전달 체계를 개선할 것으로 전망됩니다. 이 글은 SAS 글로벌 공공 부문 전략 자문위원인 제니퍼 로빈슨(Jennifer Robinson)의 글을 번역한 것입니다.(원문 보기) 이 때문에 정부 리더들은
When organizations modernize their data and analytics environments, the conversation can quickly become framed as a choice: keep the established platform or replace it with something new. But that framing can obscure the questions that matter most. Which workloads are supporting critical business decisions? What intellectual property has been built
Crunchy, versatile, and packed with nutrition, nuts and seeds are some of nature's smallest superstars. Whether you're grabbing a handful for a snack, topping a salad, or mixing them into yogurt, it's easy to add more of these nutrient-rich foods to your day. Despite their small size, nuts and seeds
For years, biodiversity has largely been viewed through an environmental lens. But for financial institutions, it's increasingly becoming a business issue. When ecosystems deteriorate, businesses that depend on them become more vulnerable. Crop yields can decline. Revenue becomes less predictable. And for banks and lenders, that means financial risk may
Organizations have spent the past several years building the foundations for AI. They’ve invested in cloud infrastructure, data platforms, copilots and, increasingly, AI agents. Now comes the harder question: What happens when those investments begin influencing real business decisions? Moving AI from experimentation into everyday operation raises the stakes. Organizations
The classical multivariate normal (MVN) distribution is a standard model for correlated data. It is a simple model, it is easy to fit the MVN model to data, and the parameters in the model (locations and correlations) are intuitive. Of course, normality is a strong assumption that is not always
La adopción acelerada de la Inteligencia Artificial en América Latina, está redefiniendo el panorama de las organizaciones en todos los sectores. Sin embargo, respaldados por nuestros 50 años de historia y continuidad en la innovación analítica, en SAS sabemos que la velocidad sin control carece de valor a largo plazo.
An AI system recommends an action. A person reviews the reasoning, considers the context and decides whether to proceed. That interaction captures one of the central questions facing organizations today: As AI moves from generating answers to taking action, how do we determine how much autonomy to give it? The
Some of the best ideas don't start with a finished solution. They start with a problem worth solving and the opportunity to experiment. The SAS Hackathon is a monthlong global innovation experience that brings together partners, customers and SAS experts to tackle real business challenges using data and AI. Along
Health care has never measured quality more aggressively. Organizations track thousands of measures across clinical quality, patient experience, access and outcomes. From readmissions and medication adherence to patient satisfaction and preventive care, organizations have more ways than ever to understand how well care is being delivered. Yet despite having more
“It’s not a technology problem. It’s a process problem.” “It’s not an accuracy issue. It’s an adoption issue.” “It’s not the model. It’s the human.” Sound familiar? I hear these arguments all the time. But here’s the reality: generative AI has an accuracy problem. In fact, it is well documented
A previous article discusses how to generate a random correlation matrix. On average, in a random correlation matrix, half of the off-diagonal entries are negative and half are positive. For any realization, the proportion of negative correlations might be greater than (or less than) half. This is in contrast to