Opsera announced that two new patents have been issued for its Unified DevOps Platform, now totaling nine patents issued for the cloud-native DevOps Platform.
With the release of SAS Open Model Manager in November, SAS is helping organizations operationalize open source models and put their data to work for smarter, faster business decisions.
SAS Open Model Manager helps organizations streamline the process for analytical models to go quickly from the lab into production, and closely monitors and revalidates the performance of these models.
Bringing together data scientists and IT/DevOps, SAS Open Model Manager helps organizations register, deploy and monitor open source models in one central environment. The solution offers seamless integration with Python and R. Users can compare and assess different models, manage champion and challenger models, and access built-in performance reports to quickly evaluate whether to retrain, retire or develop new models.
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 also improves governance by helping users better understand the function and performance of deployed models over time. Without the ability to continuously monitor a model's degradation, business value and opportunity is rapidly lost.
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. Designed specifically to meet the needs of the open source community, no additional SAS technology is needed.
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. Organizations that fail to embrace ModelOps face increasing challenges in scaling analytics and fall short of the competition.
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