SAS^® Model Manager

Features

Central, secure repository for managing analytical models

• Project-based storage of models.
  • Set up and maintain separate versions of champion and challenger models within a project:
    • Freeze a version.
    • Set a default version for the project.
    • Champion-challenger model promotion.
  • Map prerequisite data sources used for model reporting and score code testing:
    • Training and test tables.
    • Score input and output tables.
    • Performance tables.
    • Project input and output tables.
  • Accounting and auditability:
    • Event logging of all major actions.
    • User-defined notes.
    • Attach documents (Microsoft Word documents, Microsoft Excel spreadsheets, HTML files, etc.), and add version control.
• Prebuilt templates for registering standard data mining models:
  • Prediction.
  • Segmentation.
  • Classification.
  • Scorecards.
  • User-defined template.
  • Optional batch model registration support for bulk loading.
  • General properties such as model name, type of algorithm, creation date, modification date, etc.
  • Advanced view of SAS^® Enterprise Miner™ process flow diagram.
• Provide more control in setting input and output variables to define the project.
• Import multiple Base SAS, SAS/STAT^® and SAS Enterprise Miner models:
  • Training code.
  • Score logic.
  • Estimate tables.
  • Target and input variable.
• Import and export PMML model code with inputs and outputs.
• Register, compare, report, score and monitor models built in R.
• Repository metadata summary report with information such as:
  • Number of models; number of scoring jobs.
  • Model-aging profiles.
  • Frequency counts of how often each target and input variable has been used across the model portfolio.
• Query the model repository by attributes, such as:
  • Type of algorithm.
  • An input or target variable.
  • Model creator.
  • Model ID.
  • Lifecycle assignee or approval user.
  • Combination of query attributes.
  • Ability to add user-defined query keys.
• Secure, reliable model storage and access administration:
  • Backup and restore capabilities.
  • Overwrite protection.
  • Event logging.
  • User authentication/access privilege administration.

Analytical workflow management

• Create custom processes for each model using SAS Workflow Studio:
  • Automated notifications provide collaboration across teams.
  • Define, manage and track complete analytic life cycles.
  • A Web interface enables enterprise access and collaboration.
  • Process management capabilities create efficiency.

Scoring-logic validation before models are exported to production

• Define test and production score jobs:
  • Map required inputs and outputs.
  • Add pre- and post-SAS code.
  • View log and results table.
  • Create interactive graphs.
• Export models to SAS Metadata Repository.
• Production scoring:
  • Mining Results Transformation available in SAS Data Integration Studio.
  • Model Scoring Task available in SAS^® Enterprise Guide^®.
  • Publish models directly to SAS Real-Time Decision Manager.
• Publish model updates to different scoring channels:
  • Email notification sent to subscribers.
  • Store results to a file system or post to a corporate intranet.
• In-database model deployment:
  • Integrated with SAS Scoring Accelerator for Teradata, SAS Scoring Accelerator for Netezza and SAS Scoring Accelerator for IBM DB2 to publish and validate scoring functions for native scoring in those databases.

Monitoring and reporting on model performance during test and production life cycles

• Model performance reports:
  • Variable distribution plots.
  • Characteristic charts.
  • Stability charts.
  • Lift charts.
  • Receiver Operating Curve and Gini charts.
  • Kolmogorov-Smirnov charts.
• Model comparison reports:
  • Model profile reports.
  • Delta reports.
  • Dynamic lift reports.
  • Model monitoring reports.
  • Ad hoc SAS Code Report editor.
  • HTML, RTF, PDF and Microsoft Excel output formats.
• GUI for creating performance-monitoring reports.
• Easy-to-use wizard for creating performance-monitoring dashboards.
• Model retraining:
  • Create new challenger models based on SAS Enterprise Miner models currently registered in a project, and new data and variables.

Overall lifecycle management of analytical models

• Model lifecycle templates for collaborative project management:
  • Basic, standard, extended and user-defined.
  • Model Lifecycle Template editor for user-defined.
  • Task-oriented milestone completion and approval signoff.
• Progress-completion status reports.