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A TFX component to validate input examples.

Used in the notebooks

Used in the tutorials

The ExampleValidator component uses Tensorflow Data Validation to validate the statistics of some splits on input examples against a schema.

The ExampleValidator component identifies anomalies in training and serving data. The component can be configured to detect different classes of anomalies in the data. It can:

  • perform validity checks by comparing data statistics against a schema that codifies expectations of the user.
  • run custom validations based on an optional SQL-based config.

Schema Based Example Validation The ExampleValidator component identifies any anomalies in the example data by comparing data statistics computed by the StatisticsGen component against a schema. The schema codifies properties which the input data is expected to satisfy, and is provided and maintained by the user.


# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(

Component outputs contains:

See the ExampleValidator guide for more details.

statistics A BaseChannel of type standard_artifacts.ExampleStatistics.
schema A BaseChannel of type standard_artifacts.Schema. required
exclude_splits Names of splits that the example validator should not validate. Default behavior (when exclude_splits is set to None) is excluding no splits.
custom_validation_config Optional configuration for specifying SQL-based custom validations.

outputs Component's output channel dict.