Evaluating AI models

You can track and measures outcomes from your AI models to help ensure that they are compliant with business processes no matter where your models are built or running.

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

Enterprises use model evaluations as part of 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 AI models are free from bias, can be easily explained and understood by business users, and are auditable in business transactions.

Watch this short video to learn more about model evaluations.