Streamline the model life cycle. Deploy models everywhere. Connect data scientists and IT. And get the most value from your analytics investments.
Ensure model governance and transparency.
A centralized, searchable repository for all types of models and analytical assets gives you complete visibility into your analytical processes, ensuring traceability and governance. The solution simplifies model management with life cycle templates and version control, enabling you to track project history through each step of the model management process and get a unified view of each model’s currency, definition and value. Using open REST APIs to access models and model-score artifacts streamlines IT work.
Easily validate models to ensure high-quality predictions.
SAS Model Manager automatically generates executable scoring code for Python-based models. You can easily test models, validating model scoring logic before models are pushed into production using a precise methodology and a system that automatically records each test the scoring engine performs.
Build once, deploy everywhere – no additional testing required.
Efficiently move your analytical models from the innovation lab into your chosen production environment one click. This eliminates having to recode models written in various languages for deployment, testing the results to ensure they perform as expected, then repeating that cycle next time the model needs to be refreshed. Rapid, automated model deployment enabless you to easily deploy models into your business processes – in batch or real time, in the cloud or at the edge.
Automatically monitor model performance to keep them performing as expected.
SAS Model Manager automatically monitors model performance from inception, to usage, to retirement, regardless of the language used to create them. Performance benchmarking reports display the champion model’s scoring performance and document conformity to required standards. Alerts are generated to indicate model decay. As models are used across different departments, the solution produces extensive tracking, validation and auditing reports, and marks champion models for use in other applications. Ongoing monitoring lets you know when it’s time to refine or retire a model.
Increase efficiency by adapting models to reflect internal or external changes.
Continuously update models to keep pace with changing market and business conditions. You can retrain the existing model on new data, or revise the model using feature engineering or new data elements. Model retraining integrates with the model pipeline processing environment for greater efficiency.
Save time and resources by automating the model life cycle using a CI/CD approach.
SAS Model Manager enables you to integrate multiple environments, tools and applications using open REST APIs. You can automate the analytic life cycle by creating custom workflows that match your business requirements and processes.
Simplify model collection creation and management with a web-based interface that easily automates the model management process.
Get to Know SAS® Model Manager
SAS Model Manager helps you get faster value from your analytic investments by taking a ModelOps approach to getting analytics out of the lab and into use.
Conquer all your analytics challenges – from experimental to mission critical – with faster decisions in the cloud. The latest release of SAS Viya is now available on these cloud providers.
Microsoft is our strategic partner and preferred cloud provider. With deep integration and a shared road map, SAS and Microsoft are shaping the future of AI and analytics in the cloud.
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