SAS ModelOps: From Decisions to Recommendations

Organizations have a hard time deploying analytical models consistently. Only around half of all models are ever put into production, according to various studies. The remaining half is shelfware and hence adds no value to the business. As a result, the "ModelOps" challenge has expanded dramatically. Recognizing these difficulties, SAS Viya employs three key strategies: model management, decision management, and operationalization.

A developing area of interest, based on ModelOps, has been offering personalized suggestions to enterprises across a variety of use cases. This form of contact is similar to strategies used by Amazon, Google, and Netflix to serve their specific consumer groups. These "Recommender Systems," which are based on Machine Learning, Deep Learning, and other analytic methodologies, are one of the cornerstones of "True" Artificial Intelligence (AI)..

About the Presenter

Gene Grabowski, Jr. works for SAS' Manufacturing, Transportation, and Entertainment Services (MTES) Business Unit as an Advisory Solutions Architect and Data Scientist. He specializes in developing analytic solutions for clients and pursues all aspects of the analytics lifecycle. Gene worked for the Ford Motor Company and Ford Credit for 13 years before joining SAS. Today, he spends his time integrating Open-Source tools with SAS and deploying Machine Learning models. In addition, he performed as a guest lecturer at Oakland University, Wayne State, Carnegie Mellon and the University of Alabama.

Gene graduated from Michigan State University with an M.A. in Economics and Oakland University with a B.S. in Economics. He received his Machine Learning Certification from Stanford University in June 2020.