SAS moves organizations open source models beyond the lab to enable smarter, faster decisions
Summary
An IDC survey noted that less than half of organizations can claim that their analytical models are sufficiently put to work, and only 14% say that the output of data scientists is fully operationalized. Simplified publishing and scoring steps provide flexibility to deploy models with just a few clicks, both in batch and real time, with different operational environments. SAS Open Model Manager will be delivered through container-enabled infrastructures, including Docker and Kubernetes, providing a portable, lightweight image that can be deployed in private or public clouds. ModelOps is another key ingredient in the last mile of analytics, where organizations move models from the data science lab into IT production as quickly as possible while ensuring quality results. The practice of ModelOps enables organizations to manage and scale models to meet demand and continuously monitor them to spot and fix early signs of degradation.
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