With its 2.0 release, TensorFlow retains its prominence as the industry’s leading machine learning framework. TensorFlow began as a numerical processing package that gradually expanded to include libraries supporting a variety of ML approaches and execution environments, ranging from mobile CPU to large GPU clusters. Along the way, a slew of frameworks became available to simplify the tasks of network creation and training. At the same time, other frameworks, notably PyTorch, offered an imperative programming model that made debugging and execution simpler and easier. TensorFlow 2.0 now defaults to imperative flow (eager execution) and adopts Keras as the single high-level API. While these changes modernize TensorFlow's usability and make it more competitive with PyTorch, it is a significant rewrite that often breaks backward compatibility — many tools and serving frameworks in the TensorFlow ecosystem won't immediately work with the new version. For the time being, consider whether you want to design and experiment in TensorFlow 2.0 but revert to version 1 to serve and run your models in production.