SAS taps into open source with Open Model Manager

Christopher Tredger
By Christopher Tredger, Portals editor
Milan, Italy, 23 Oct 2019
Jim Goodnight, CEO of SAS, addresses delegates during the opening of the company’s Analytics Experience 2019 in Milan.
Jim Goodnight, CEO of SAS, addresses delegates during the opening of the company’s Analytics Experience 2019 in Milan.

Business analytics software vendor SAS will launch a new solution, SAS Open Model Manager, in November 2019 to help companies with the challenge of deploying analytics platforms.

In his keynote address at the SAS Analytics Experience 2019 in Milan this week, CEO Jim Goodnight said the field of data analytics and AI was full of hype and unkept promises. SAS has embarked on a campaign to highlight the advantages of real-world application of AI, advanced machine learning and natural language processing, he said.

Goodnight and other company executives outlined the 'last mile of analytics' challenge: 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 laboratory and into production.

According to SAS, many organisations struggle to complete the last mile of analytics, in part because of cumbersome manual processes and inconsistent collaboration between IT and business users.

IDC has stated that only 35% of organisations have analytical models fully deployed in production. Of those that are deployed, 90% took more than three months to be deployed, and 40% took seven months, said Tom Fisher, senior VP of business development at SAS.

Chandana Gopal, research director of business analytics at IDC, said organisations have a good handle on building and training analytical models, including open source ones, but there is often a gap when it comes to operationalising those models and pushing them into production, and a lot of the work done by data scientists is lost.

The burden of moving models from development to deployment is significantly eased by improving model development, production and automation.

“We take advantage of massively parallel computing to take analytics models out of the lab and into reality," said Goodnight. "Modellers can compute hundreds of models in the same time it takes to compute one in the traditional world… they can be a hundred times more efficient than before.”

SAS Open Model Manager, which is targeted at organisations not using SAS products and primarily using open source languages like Python and R,  is designed to help organisations working on cutting edge data science efforts put their data to work for smarter, faster business decisions. 

SAS has also launched a global initiative, aimed at operationalising the last mile of analytics. This initiative comprises SAS ModelOps, a packaged offering including SAS technology and advisory services.

"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 catalogue and deploy their analytical models," said IDC's Gopal. "With this capability organisations can harness the value of their analytical assets and improve transparency through continuous monitoring.”

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.