ModelOps: The Way Forward
ModelOps is a holistic approach for rapidly and iteratively moving models through the analytics life cycle. While ModelOps is based on the application development community's DevOps approach, where DevOps focuses on application development, ModelOps focuses on getting models from the lab, to validation, to testing, to deployment as quickly as possible, while ensuring quality results. ModelOps – which encompasses culture, processes and technology – enables you to smoothly, efficiently and continuously develop and deploy models so you can cross the last mile and ensure analytics delivers on its promise.
Elements of a ModelOps Approach
ModelOps fosters dynamic collaboration and improved productivity for analytics and IT operations teams, regardless of what analytic language is used, what data is accessed or where the model will be deployed. It gets analytics out of the lab and into use, enabling you to conquer the last mile of analytics.
Access data from a trusted source and align it to privacy and security standards.
Create models with a deployment scenario in mind to avoid re-work.
Preserve data lineage and track-back information for governance and audit compliance.
Do it all in minutes, not months, with close collaboration between data scientists and IT.
Deploy models with a monitoring mindset so analysts can monitor and retrain models as they degrade.
How SAS Enables a ModelOps Approach
Overcome your challenges related to operationalizing analytics with a solution that combines SAS Model Manager software with dedicated consulting services to jumpstart your implementation and get ongoing ModelOps guidance – regardless of the language your models are written in.
ModelOps Health Check Assessment Service
Discover what it will take to successfully operationalize analytics in your environment. Our assessment service will help you understand and define your intentions and readiness, with recommendations for consistently deriving value from your SAS and open source models.