Testing a trained AI model on data it hasn't seen before to ensure it generalizes well and doesn't just memorize training data. Like giving students a practice test before the final exam.
After training a fraud detection model, validation testing on new transactions ensures it can accurately detect fraud it hasn't encountered before.
Model validation is a general ML practice (not a single cloud service). These managed services help operationalize validation by tracking model quality on holdout/production data, detecting drift, and triggering alerts or retraining workflows.