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Chapter 12
In a TFX pipeline, a unit of data, called an artifact, is passed between components.
Normally a component has one input artifact and one output artifact. Every artifact
has an associated metadata that defines its type and properties. The artifact type
defines the ontology of artifacts in the entire TFX system, while the artifact property
specifies the ontology specific to an artifact type. Users have the option to extend the
ontology globally or locally.
TFX pipeline components
The following diagram shows the flow of data between different TFX components:
Flow of data between TFX components
All the images in the TFX section have been adapted from the
TensorFlow Extended official guide: https://www.tensorflow.
org/tfx/guide.
To begin with we have ExampleGen, which ingests the input data, and can also split
the input dataset. The data then flows to StatisticsGen, which calculates the statistics
of the dataset. Then comes SchemaGen, which examines the statistics and creates
a data schema; then an ExampleValidator, which looks for anomalies and missing
values in the data; and Transform, which performs feature engineering in the
dataset. The transformed dataset is then fed to the Trainer, which trains the model.
The performance of the model is evaluated using Evaluator and ModelValidator.
Finally, if all is well, the Pusher deploys the model on the serving infrastructure.
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