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Evaluating AI models

Evaluating AI models

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

Required service
Watson Machine Learning
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. You can evaluate machine learning models or evaluate prompt templates for foundation models, depending on which service that you use.

Watch this short video to learn more about Watson OpenScale.

This video provides a visual method to learn the concepts and tasks in this documentation.

Try a tutorial

The Evaluate a machine learning model tutorial provides hands-on experience with configuring Watson OpenScale to monitor fairness, quality, and explainability.

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Parent topic: Governing AI assets

Generative AI search and answer
These answers are generated by a large language model in watsonx.ai based on content from the product documentation. Learn more