TensorFlow

What is TensorFlow?

TensorFlow is an end-to-end open source platform for machine learning that lets developers build, train and deploy models that run in many environments. It is developed by Google and exposes a high-level Keras API.

Key capabilities of TensorFlow

• tf.keras: High-level API for building neural networks in a declarative style.
• TensorFlow Lite (LiteRT): Runs models on Android, iOS, Raspberry Pi and Edge TPU devices.
• TensorFlow.js: Trains and runs models in the browser or Node.js.
• TFX: Builds production ML pipelines with MLOps practices.
• tf.data: Preprocesses data and builds input pipelines.
• TensorBoard: Visualizes and tracks model development.
• TensorFlow Datasets: Ships ready-made standard datasets.
• TensorFlow GNN: Analyzes relational data with graph neural networks.

How TensorFlow works

Developers write Python code, often with tf.keras, to define layers, compile a model with an optimizer and loss, then train it on data prepared with tf.data. Trained models can be exported to LiteRT for mobile and edge, run in the browser with TensorFlow.js, or served through TFX pipelines in production. There is no autonomous agent step.

Who uses TensorFlow?

TensorFlow is used by machine learning engineers, researchers and data scientists. Its edge and browser runtimes suit teams shipping models to phones, embedded devices and web apps.

TensorFlow pricing

TensorFlow is free and open source, so there is no license fee. Costs come from the compute, storage and cloud services you run it on.

TensorFlow alternatives

Alternatives include PyTorch, which is a widely used open source deep learning framework, JAX, which targets high-performance numerical computing, and Keras, which can run as a high-level layer over several backends.