What is TensorFlow?
TensorFlow is an open-source deep learning framework developed by Google for building, training, and deploying neural networks. It ships with Keras as its high-level interface for defining models, and with deployment tools: a serving system for production APIs, a lightweight runtime for phones and embedded devices, and a JavaScript version for the browser. It is also the native framework for Google’s TPU accelerators.
TensorFlow was the dominant framework for several years, and a large base of production systems was built on it. In research and in the large language model ecosystem, PyTorch has since become the common choice, and Google’s own research increasingly uses JAX.
Why TensorFlow matters
Existing TensorFlow systems do not need to be rewritten. A distributor’s demand model built in TensorFlow years ago can keep running, and models can be converted to ONNX if the serving environment changes. The framework’s strength remains the path from training to a small, fast model on a device: a retailer running shelf-scanning cameras or a utility with inspection drones often uses the lightweight runtime for on-device inference. New hiring leans toward PyTorch skills, so teams with TensorFlow systems should plan for how they will maintain them.
What to watch for
- Code written for the first major version does not run unchanged on the second, which matters for older systems.
- Decide whether a new project should start in TensorFlow or PyTorch based on the team and the serving target.
- Export to ONNX if the model may need to run outside the TensorFlow runtimes.