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

At the SAS Analytics Experience 2019 in Milan, company executives outlined the challenge and said less than half of analytic models are fully deployed, 90% of them take more than three months to deploy and 40% take over 7 months to deploy.

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, the company stated.

SAS claims the Open Model Manager is designed to help organisations operationalise open source models and put their data to work for smarter, faster business decisions. The company said the solution will closely monitor and revalidate the performance of models.

The solution is said to offer seamless integration with Python and R, and "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."

In his keynote address, Jim Goodnight, chief executive officer of SAS, said the field of analytics and AI is full of hype and un-kept promises, and SAS has embarked on a campaign to highlight the advantages of real-world application of AI, advanced machine learning and natural language processing.

Examples of successful application can be found in finance, healthcare and education, he added.

Goodnight pointed to the use of creative computer vision application and sensor technology in the development of self-driving cars, and Amazon's use of these technologies to manage transactions at retail outlets. There are no checkout counters – every step of the process, from item scanning to pricing to payment is automated he said.

Last mile challenge

Goodnight also stressed that while there is an increase in the development of models, taking them from incubation to application – the last mile of analytics - remains a challenge for decision makers as they grapple with the realities of digital transformation.

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, the company explained.

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

Executives pointed to an IDC survey which noted that less than half of organisations can claim that their analytical models are sufficiently put to work, and only 14% say that the output of data scientists is fully operationalised.

Chandana Gopal, Research Director, 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. 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. 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.

The company continues to focus on key areas including AI and machine learning, IOT and customer intelligence, data management, fraud and security intelligence, as well as risk management and the cloud.