You can track and measures outcomes from your AI assets to help ensure that they are compliant with business processes no matter where your models are built or running.
You can use model evaluations as part of your AI governance strategies to ensure that models in deployment environments meet established compliance standards regardless of the tools and frameworks that are used to build and run the models. This
approach ensures that models are free from bias, can be easily explained and understood by business users, and are auditable in business transactions.
Required service
watsonx.ai Runtime
Training data format
Relational: Tables in relational data sources
Tabular: Excel files (.xls or .xlsx), CSV files
Textual: In the supported relational tables or files
Connected data
Cloud Object Storage (infrastructure)
Db2
Data size
Any
Watch this short video to learn more about model evaluations.
This video provides a visual method to learn the concepts and tasks in this documentation.
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