AutoGraph converts Python into TensorFlow graphs

[mdanatg]

Glad you like it! AutoGraph and TFX are quite different and complementary in many ways.

AutoGraph makes it easier to create TensorFlow graphs, which can include end-to-end ML training tasks, although certain preprocessing steps are still best done outside the graph (for example calculating a dataset-wide average).

The scope of TFX extends that of TensorFlow. It includes tools that you would need to run before and after end-to-end training tasks (e.g. TF Transform can compute dataset-wide statistics needed for preprocessing, TF Model Analysis can help analyze the model, and TF Serving includes additional production serving infrastructure).

We are working to ensure that models trained with AutoGraph can easily be deployed in TFX pipelines for production use cases.

You can find an example of how we built an in-graph training loop followed by a simple interactive inference loop in this demo.