Understanding TensorFlow Enterprise

TensorFlow has become an ecosystem consisting of many valuable assets. At the core of its popularity and versatility is a comprehensive machine learning library and model templates that evolve quickly with new features and capabilities. This popularity comes at a cost, and that cost is expressed as complexity, intricate dependencies, and API updates or deprecation timelines that can easily break the models and workflow that were laboriously built not too long ago. It is one thing to learn and use the latest improvement in your code as you build a model to experiment with your ideas and hypotheses, but it is quite another if your job is to build a model for long-term production use, maintenance, and support.

Another problem associated with early TensorFlow in general concerned its code debugging process. In TensorFlow 1, lazy execution makes it rather tricky to test or debug your code because the code is not executed unless it is wrapped in a session...