SAS Unites Data Scientists and IT/ DevOps Working from Home
Attention IT/devops and data scientists who are stuck like the rest of the world working from home. Dashboards these days are becoming a dime a dozen now. The accelerated adoption of AI and machine learning, combined with the accessibility of open source software has data scientists churning out more analytical models than ever. Real value comes from analytic models and the real-real value come from deploying them into production.
Data scientists put a lot of work into creating models for their organisations, but a surprisingly small percentage find their way into production. SAS Open Model Manager changes that.
According to an IDC survey, less than half of organisations say their analytical models are sufficiently put into production, and only 14% say their data scientists' work is fully operationalised.
SAS Open Model Manager is intended to help organisations operationalise their open source models so they can better put their data to work.
SAS Open Model Manager Allows you to More Expediently Register, Deploy into Production and Monitor Open Source Analytic Models
One Central Environment Unites Data Scientists and IT/ DevOps
During the on-going Covid-19 Pandemic, with everyone around the world working from home, SAS recognizes that it’s even harder for teams to unify in analytic model development and deployment. Add to this the plethora of open-source (Python) models and companies have a real dilemma trying to rapidly build and register, score, deploy, monitor performance of models.
To easily operationalize your open source analytical models, and put your data to work for faster, smarter business decisions, SAS Open Model Manager enables organizations to quickly and easily publish, validate and deploy models into production.
Centrally store and manage your open source models, regardless of the programming language used to create them – with complete traceability all the way back to the source. SAS Open Model Manager makes it easy to understand your analytical models' definition, properties and function – including who is testing, validating and approving different models – while fostering collaboration between DevOps, data scientists and business users.
Models begin to degrade the moment you put them in production. With SAS Open Model Manager, you can monitor the performance of your analytical models to see if they continue to behave as expected after changes in market conditions or customer behavior, new data becomes available or there is concept drift. By monitoring their performance, you can avoid model decay and revalidate the business value of your models in production to keep them performing at the highest levels.
"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," according to IDC research director for business analytics Chandana Gopal.
"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 organisations can harness the value of their analytical assets and improve transparency through continuous monitoring."
Containerized (Docker, Kubernetes) analytics model management captures all the environmental dependencies for the analytic workload, and you can take advantage of distributed environments to deploy models at scale. Since the solution is designed specifically to meet the needs of the open source community, no additional SAS technology is needed.