Continuously validating software and bringing new functionality to customers faster requires vast amounts of data.

If you have already adopted graph, it is important to take an inventory of the actual schema of your graph. Ideally data that lands in a database adheres to your intended data model.

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You can customize your own prompt datasets and scoring algorithms specific for your generative AI application. Learn how to build and prepare your own datasets for FM evaluation on Amazon SageMaker.

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Amazon SageMaker Clarify now supports foundation model (FM) evaluation to help you quickly evaluate, compare, and select the best FM for your use case based on a variety of criteria across different

Some evaluation criteria are nuanced or subjective and require human judgement to assess.

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Amazon SageMaker Clarify automatic FM evaluation is available as an open-source library in GitHub to allow you to run it anywhere.