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V3.2 Release Notes

V3.2 Release Notes

Owned by Dave Trier
Last updated: Aug 04, 2023

What’s New:

ModelOp Center v3.2 introduces support for Generative AI models, including Large Language Models (LLM’s), providing comprehensive AI governance for these transformational models. Additionally, v3.2 introduces support fine-grained access controls, allowing for setting read/write/execute permissions at the model, snapshot, notification, job, and test result level across groups.

Governance & Security:

  • Generative AI Inventory: added support for classifying models as Generative AI (including LLM’s) as well as creating Generative AI ensembles, including managing langchain models, prompt templates, embedding models, LLM’s, and validation code (guardrails)

  • Generative AI Asset Management: added support for prompt templates, RAILS, etc. for Generative AI asset tracking

  • Granular Entity-Level Security: extending the current group-based isolation security model to provide fine-grain access control at a given entity-level (e.g. Snapshot, Job), such that read/write/execute privileges can be set at an individual entity level, as desired.

  • Google Cloud Storage Buckets: added support for managing technical artifacts (model binaries/weights, etc.) in Google Cloud Storage Buckets

 

Test, Monitor, & Visualize

  • REST-based Data Set Support: added support to pull model-specific data sets (e.g. training data, production data) via REST, allowing integration with existing REST-based data management systems

  • Testing/Monitoring Updates:

    • Added support for Rank Order Break

    • Added support for performance metrics for Probability of Default for Credit Models

    • Added support for longitudinal tracking of metrics

  • External Monitors: added support for collecting existing metrics for a model that are calculated from external monitors, with the ability to automate threshold comparison and remediation pathways.

 

General:

  • User Experience: minor User Interface enhancements including:

    • MLC tracking enhancements

    • Native Dashboard updates for usability

  • Model Archival: ability to archive a model snapshot and all its related artifacts, allowing for more clean visibility into active models while maintaining auditability.

 

Specific Details: