Sundaresh Sankaran, with the Global Technology Practice at SAS, loves to help organisations make better strategic and operational decisions, through well-designed and rigorous analytical systems and technology. Having long resisted any idea of defining boundaries to his role at SAS, he is experienced in solutions architecture, product management, customer advisory, enablement, and implementation. Proficient in technologies across the entire analytical lifecycle, he has worked with analytics teams at General Electric and Genpact, as well as the Confederation of Indian Industry prior to joining SAS.
Wouldn’t it be cool if we establish a mechanism that provides more data scientists easy access to SAS Reinforcement Learning capabilities, from a centralized location and using a standardized approach?
The Proc Python procedure, Python code editor & Python code step facilitate low-code analytics calling Python and SAS from a common interface. Data scientists also appreciate the connection to Python & R through the Model Studio Open-source Code node. Older methods of interaction include the swat and sas_kernel packages running on Python clients.
SAS Conversation Designer is available with every offering that also includes SAS Visual Analytics. Users can easily access Visual Text Analytics capabilities from SAS Conversation Designer with minimum additional configuration.
