RIME v14 Changelog

AI Firewall

Data Imputation - The AI Firewall now automatically corrects model erroneous datapoints in real-time to prevent model downtime and reduce model performance deviations.
Firewall Score - Every datapoint that is passed to the AI Firewall is now given a Firewall Score that is a holistic measure of its quality.

Key Insights - The most significant underlying data errors that may be causing a degradation in model performance are now surfaced in the main Continuous Testing results page. Attribution of model behavior declines is now easier than ever.
Production SDK - Continuous Testing is ready for production deployment with full API support. This includes functionality to create CT endpoints, incrementally upload data, and query results.

Selectable Performance Metrics - Users now have the flexibility to choose between performance metrics used in Tabular Abnormal Input and Subset Performance tests.
Additional LTR Metric - There is now a new performance metric (NDCG at K) in the LTR use case in addition to Mean Reciprocal Rank and Rank Correlation. The new NDCG at K metric is the new default metric used in the LTR use case is applied across Abnormal Input and Subset Performance tests.
RIME Images - RIME now supports the Image Classification use case. Reach out to the RI team to schedule a demo.
Custom Tests - Users can now create their own custom stress tests and run them on RIME.

RIME Hosted Trial - Trials can now be conducted in a secure hosted environment that removes the requirement to download RIME.
Performance Improvements - There have been general improvements to RIME Performance that have led to a large increase in RIME production performance.
- RAM Usage Improvements - RIME Engine has been optimized to use less memory to prevent OOM errors on data upload.
- Partitioned Dataset Upload - Users can now upload large partitioned datasets to RIME.