New Solution Streamlines Analytical Model Management to Address ‘Last Mile’ Business Challenge
The accelerated adoption of AI and machine learning, paired with the accessibility of open source software has data scientists churning out more analytical models than ever. However, there hasn’t been a corresponding increase in business value since few models make it out of the lab and into production. SAS, the leader in analytics, wants to change this. With the release of SAS Open Model Manager, SAS is helping organizations operationalize open source models and put their data to work for smarter, faster business decisions.
Many organizations struggle to complete the last mile of analytics, in part because of cumbersome manual processes and inconsistent collaboration between IT and business users. The burden of moving models from development to deployment is significantly eased by improving model development, production and automation.
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.* 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.
“Organizations have a good handle on building and training analytical models, including open source ones, but there is often a gap when it comes to operationalizing those models and pushing them into production, and a lot of the work done by data scientists is lost,” said Chandana Gopal, Research Director, Business Analytics at IDC. “There is a need in the market for a new generation of model management solutions that allow data scientists to develop models in any language of their choice, and to properly catalog and deploy their analytical models. With this capability organizations can harness the value of their analytical assets and improve transparency through continuous monitoring.”
Realizing business value with model management
Philippines-based Globe Telecom faced model deployment challenges. While the mobile and broadband provider was implementing models in both SAS and open source, its process was manual, slow and lacked governance. With SAS, Globe has dramatically reduced deployment time while seamlessly working in both SAS and open source software.
“Globe uses analytical models to make the right offers to our 65 million customers, to build and strengthen our relationships with them, and to drive better and faster business decisions,” said Dan Natindim, VP and Enterprise Data Officer at Globe Telecom. “With SAS, Globe analyzes all the data available, including customer, billing and network data, and through SAS and open source analytical models, we work to meet each customer’s individual needs.”
Registering, deploying and monitoring open source 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. Available in November, 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.
Today’s announcement was made at the Analytics Experience conference in Milan, Italy, a business technology conference presented by SAS that brings together thousands of attendees on-site and online to share ideas on critical business issues.
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