Introduction to TensorFlow

TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud. See the sections below to get started.

TensorFlow

Learn the foundations of TensorFlow with tutorials for beginners and experts to help you create your next machine learning project.

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For Web

Use TensorFlow.js to create new machine learning models and deploy existing models with JavaScript.

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For Mobile & Edge

Run inference with LiteRT on mobile and embedded devices like Android, iOS, Edge TPU, and Raspberry Pi.

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For Production

Deploy a production-ready ML pipeline for training and inference using TFX.

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Prepare and load data for successful ML outcomes

Data can be the most important factor in the success of your ML endeavors. TensorFlow offers multiple data tools to help you consolidate, clean and preprocess data at scale:

Standard datasets for initial training and validation
Highly scalable data pipelines for loading data
Preprocessing layers for common input transformations
Tools to validate and transform large datasets

Additionally, responsible AI tools help you uncover and eliminate bias in your data to produce fair, ethical outcomes from your models.

Try it in Colab

Load and preprocess an image dataset Investigate and visualize datasets

Build and fine-tune models with the TensorFlow ecosystem

Explore an entire ecosystem built on the Core framework that streamlines model construction, training, and export. TensorFlow supports distributed training, immediate model iteration and easy debugging with Keras, and much more. Tools like Model Analysis and TensorBoard help you track development and improvement through your model’s lifecycle.

To help you get started, find collections of pre-trained models at TensorFlow Hub from Google and the community, or implementations of state-of-the art research models in the Model Garden. These libraries of high level components allow you to take powerful models, and fine-tune them on new data or customize them to perform new tasks.

Try it in Colab

Train a neural network to classify images Retrain an image classifier with transfer learning

Deploy models on-device, in the browser, on-prem, or in the cloud

TensorFlow provides robust capabilities to deploy your models on any environment - servers, edge devices, browsers, mobile, microcontrollers, CPUs, GPUs, FPGAs. TensorFlow Serving can run ML models at production scale on the most advanced processors in the world, including Google's custom Tensor Processing Units (TPUs).

If you need to analyze data close to its source to reduce latency and improve data privacy, the LiteRT framework lets you run models on mobile devices, edge computing devices, and even microcontrollers, and the TensorFlow.js framework lets you run machine learning with just a web browser.

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Serve a model with TensorFlow Serving

Implement MLOps for production ML

The TensorFlow platform helps you implement best practices for data automation, model tracking, performance monitoring, and model retraining.

Using production-level tools to automate and track model training over the lifetime of a product, service, or business process is critical to success. TFX provides software frameworks and tooling for full MLOps deployments, detecting issues as your data and models evolve over time.