TFX

TensorFlow Extended (TFX) is a
Google-production-scale machine learning platform based on TensorFlow. It
provides a configuration framework to express ML pipelines consisting of TFX
components. TFX pipelines can be orchestrated using
Apache Airflow and
Kubeflow Pipelines. Both the components themselves
as well as the integrations with orchestration systems can be extended.

TFX components interact with a
ML Metadata backend that keeps a record
of component runs, input and output artifacts, and runtime configuration. This
metadata backend enables advanced functionality like experiment tracking or
warmstarting/resuming ML models from previous runs.

Documentation

Please see the
TFX User Guide.

Examples

• Chicago Taxi Example

Compatible versions

The following table describes how the tfx package versions are compatible with
its major dependency PyPI packages. This is determined by our testing framework,
but other untested combinations may also work.