Understand model scoring

Model scoring is the process of using a deployed model to generate predictions based on input data. In H2O MLOps, once a model version is deployed, you can send data to the deployment and receive predictions in response.

H2O MLOps provides multiple ways to perform scoring:

• Quick scoring: A UI-based option to test your deployment with sample input data.
• Deployment scorer: A Python client–based option for scoring against deployments.

To learn more about scoring methods and how to use them, see:

• Quick scoring
• Deployment scorer

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