[Editor's note: this post was co-authored by Marinela Profi and Wilbram Hazejager] Data science teams are multidisciplinary, each with different skills and technologies of choice. Some of them use SAS, others may have analytical assets already built in Python or R. Let's just say each team is unique. As part
Think about what a modern implementation of SAS looks like for a customer. Programmers rely on robust environments to run the models and programs that answer business questions. These environments can be different for platforms like SAS® 9 and SAS® Viya®. They can be deployed across distributed servers, either on
SAS Analytics Pro consists of three core elements of the SAS system: Base SAS®, SAS/GRAPH® and SAS/STAT®. The containerized deployment option adds the full selection of SAS/ACCESS engines making it even easier to work with data from virtually any source.
