Tensor Flow is an open-source ML platform (developed by Google) used for building and training various types of ML models including deep learning models. It is a flexible architecture that facilitates computation across a variety of platforms (desktops, servers, mobile and edge devices). It is used both by novices and professionals. It has rich resources and documentation.

To begin with, TensorFlow had a static computation graph. However, since TensorFlow 2.0, it has become dynamic like PyTorch. It is easier to use and debug now. Though PyTorch is considered user-friendly and a simpler API, TensorFlow has a steeper learning curve with a complex API and abstraction layers.

TensorFlow, of course, has a wider community of users — both industry and academia, since it is Google-backed. PyTorch is catching up.

TensorFlow has a mature ecosystem for deploying models in production. There are tools like TensorFlow Serving and TensorFlow Lite. They suit different environments (mobile and embedded devices). PyTorch is catching up.

Choosing between TensorFlow and PyTorch is a matter of personal choice.