The ModelValidator TFX Pipeline Component helps you validate your exported models, ensuring that they are "good enough" to be pushed to production.
ModelValidator compares new models against a baseline (such as the currently serving model) to determine if they're "good enough" relative to the baseline. It does so by evaluating both models on an eval dataset and computing their performance on metrics (e.g. AUC, loss). If the new model's metrics meet developer-specified criteria relative to the baseline model (e.g. AUC is not lower), the model is "blessed" (marked as good), indicating to the Pusher that it is ok to push the model to production.
- An eval split from ExampleGen
- A trained model from Trainer
- Emits: Validation results to ML Metadata
Using the ModelValidator Component
Typical code looks like this:
import tfx import tensorflow_model_analysis as tfma from tfx.components.model_validator.component import ModelValidator ... model_validator = ModelValidator( examples=example_gen.outputs['output_data'], model=trainer.outputs['model'])