In terms of deploying trained models, you can probably get away with using TensorFlow Serving and let Kubernetes handle the orchestration and scaling part of th

In terms of deploying trained models, you can probably get away with using TensorFlow Serving and let Kubernetes handle the orchestration and scaling part of the job. I do agree that there is certainly a need to have a tool that glues all these different bits and pieces together for improving the process of taking a model from development to production.