Thanks! I'm not sure that Yahoo's TF on Spark is fully baked, but maybe its users can tell me more about that. I know it's not commercially supported. That's m

Thanks! I'm not sure that Yahoo's TF on Spark is fully baked, but maybe its users can tell me more about that. I know it's not commercially supported. That's more a business model distinction, but it has technical consequences. Skymind built the DL4J stack from the ground up, so it's easy for us to maintain and extend, and we give enterprise a way to derisk adoption with a support contract. There is no equivalent offering for TensorFlow outside of GCE, let alone TF on Spark. In the end, it's more useful to think of DL4J as a complement to the Python ecosystem, rather than competing with it. We import TF models through Keras, and soon we'll import them directly to help people productionize the models they train with TF. That's important for large orgs committed to a JVM compute environment.