SAS
About SAS
SAS Model Manager enhances model management for data scientists and MLOps engineers. It provides an intuitive interface for creating, validating, deploying, and monitoring models. This platform streamlines collaborative processes, ensuring model governance and transparency while minimizing deployment headaches, making it an essential tool in analytics.
SAS Model Manager offers flexible pricing tiers, allowing users to choose based on their needs. Each tier provides models’ lifecycle management, deployment efficiency, and monitoring capabilities. Upgrading unlocks advanced features and specialized support, allowing teams to maximize the impact of their AI initiatives with better model control.
SAS Model Manager features a user-friendly interface designed for seamless navigation. Its organized layout ensures users can quickly access modeling assets and tools. Built-in explainability features enhance user interaction, while the straightforward design helps data scientists and MLOps engineers to efficiently manage their modeling processes.
How SAS works
Users begin their journey with SAS Model Manager by onboarding through an intuitive setup process. Navigating the platform, they connect datasets, create models, and utilize advanced features for validation and deployment. With automation options for CI/CD and ongoing monitoring capabilities, users can efficiently track model performance and adapt as needed.
Key Features for SAS
Automated CI/CD Pipeline
SAS Model Manager’s automated Continuous Integration and Continuous Delivery (CI/CD) pipeline ensures efficient model deployment. This unique feature streamlines updates and helps teams minimize manual efforts. By standardizing processes, SAS Model Manager empowers organizations to focus on impactful analytics and effective model management.
Model Validation with No-Code Interface
SAS Model Manager provides a no-code interface for easy model validation, enabling users to test scoring logic effortlessly. This feature reduces the complexities of model assessment, allowing data scientists to efficiently ensure operational readiness and accuracy of their analytics models for deployment in production environments.
Real-time Monitoring and Alerts
With built-in monitoring features, SAS Model Manager illuminates data and model drift, sending real-time alerts. This functionality empowers users to proactively address model decay and maintain accuracy over time. The continuous monitoring capability ensures that models remain compliant and effective within target environments, enhancing overall model governance.
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