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I work on ML infrastructure at some big company, so I'm always interested to see whats new in this field. This seems to be a thin wrapper around tf.estimator, which provides a majority of the functionality. The only novel things are yaml config for defining some basic transformations and data format. It doesn't seem super useful, am I missing something?

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The main thing we try to help with is orchestrating Spark, TensorFlow, TensorFlow Serving, and other workloads without requiring you to manage the infrastructure. You’re right that we have a thin layer around tf.estimator (by design) because our goal is to make it easy to create scalable and reproducible pipelines from building blocks that people are familiar with. We translate the YAML blocks into workloads that run as a DAG on Kubernetes behind the scenes.

curious what kind of infrastructure have you built for deployment, serving and managing models?

We use TesnorFlow serving (https://www.tensorflow.org/serving) for serving the trained models. We also run Flask to transform the incoming JSON to match the way the data has been transformed at training time.

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