TFX is an end-to-end platform for deploying production ML pipelines

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

User Documentation

Please see the TFX User Guide.

Development References

Roadmap

The TFX Roadmap, which is updated quarterly.

Release Details

For detailed previous and upcoming changes, please check here

Requests For Comment

For designs, we started to publish RFCs under the Tensorflow community.

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.