Connect data scientists, MLOPs engineers and business analysts. Deploy models quickly. And integrate with open source.
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 version control. You can 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.
Use our open source package, sasctl, to automatically generate executable scoring code for Python-based models. You can easily test models, validating model scoring logic before models are pushed into production, from an easy-to-use no-code interface.
Build once, deploy everywhere – no recoding required.
Efficiently move your analytical models from the innovation lab into your chosen production environment. SAS Model Manager has you covered, whether your need to: deploy models into databases; score data in batch; host a real-time REST API scoring endpoint; push models into a container hosted in registries on Docker, Azure, GCP or AWS; or deploy directly into Azure Machine Learning.
Automatically monitor model performance to keep them performing as expected.
SAS Model Manager automatically monitors the performance of models – from inception, to usage, to retirement – regardless of the language used to create them. Performance benchmarking reports display models’ 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.
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