The ExampleValidator pipeline component identifies anomalies in training and serving data. It can detect different classes of anomalies in the data. For example it can:
- perform validity checks by comparing data statistics against a schema that codifies expectations of the user.
- detect training-serving skew by comparing training and serving data.
- detect data drift by looking at a series of data.
- perform custom validations using a SQL-based configuration.
The ExampleValidator pipeline component identifies any anomalies in the example data by comparing data statistics computed by the StatisticsGen pipeline component against a schema. The inferred schema codifies properties which the input data is expected to satisfy, and can be modified by the developer.
- Consumes: A schema from a SchemaGen component, and statistics from a StatisticsGen component.
- Emits: Validation results
ExampleValidator and TensorFlow Data Validation
ExampleValidator makes extensive use of TensorFlow Data Validation for validating your input data.
Using the ExampleValidator Component
An ExampleValidator pipeline component is typically very easy to deploy and requires little customization. Typical code looks like this:
validate_stats = ExampleValidator( statistics=statistics_gen.outputs['statistics'], schema=schema_gen.outputs['schema'] )
More details are available in the ExampleValidator API reference.