# Introduction to TensorFlow # 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. ![](https://www.tensorflow.org/static/site-assets/images/marketing/learn/learn-core.svg) #### TensorFlow Learn the foundations of TensorFlow with tutorials for beginners and experts to help you create your next machine learning project. [Learn more](https://www.tensorflow.org/tutorials) ![](https://www.tensorflow.org/static/site-assets/images/marketing/learn/learn-web.svg) #### For Web Use TensorFlow.js to create new machine learning models and deploy existing models with JavaScript. [Learn more](https://www.tensorflow.org/js) ![](https://www.tensorflow.org/static/site-assets/images/marketing/learn/learn-mobile.svg) #### For Mobile \& Edge Run inference with LiteRT on mobile and embedded devices like Android, iOS, Edge TPU, and Raspberry Pi. [Learn more](https://ai.google.dev/edge/litert) ![](https://www.tensorflow.org/static/site-assets/images/marketing/learn/learn-production.svg) #### For Production Deploy a production-ready ML pipeline for training and inference using TFX. [Learn more](https://www.tensorflow.org/tfx) ## An end-to-end platform for machine learning ### 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](https://www.tensorflow.org/datasets) for initial training and validation - Highly scalable [data pipelines](https://www.tensorflow.org/guide/data) for loading data - [Preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers) for common input transformations - Tools to [validate](https://www.tensorflow.org/tfx/guide/tfdv) and [transform](https://www.tensorflow.org/tfx/guide/tft) large datasets Additionally, [responsible AI](https://www.tensorflow.org/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](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/load_data/images.ipynb) [Investigate and visualize datasets](https://colab.research.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/data_validation/tfdv_basic.ipynb) ### Build and fine-tune models with the TensorFlow ecosystem Explore an entire ecosystem built on the [Core framework](https://www.tensorflow.org/guide/basics) that streamlines model construction, training, and export. TensorFlow supports distributed training, immediate model iteration and easy debugging with [Keras](https://keras.io/), and much more. Tools like [Model Analysis](https://www.tensorflow.org/tfx/guide/tfma) and [TensorBoard](https://www.tensorflow.org/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](https://www.tensorflow.org/hub) from Google and the community, or implementations of state-of-the art research models in the [Model Garden](https://www.tensorflow.org/guide/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](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/classification.ipynb) [Retrain an image classifier with transfer learning](https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_image_retraining.ipynb) ### 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](https://www.tensorflow.org/tfx/guide/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](https://ai.google.dev/edge/litert/guide#1_choose_a_model) framework lets you run models on mobile devices, edge computing devices, and even microcontrollers, and the [TensorFlow.js](https://www.tensorflow.org/js) framework lets you run machine learning with just a web browser. #### Try it in Colab [Serve a model with TensorFlow Serving](https://colab.research.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/serving/rest_simple.ipynb) ### 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](https://www.tensorflow.org/tfx) provides software frameworks and tooling for full MLOps deployments, detecting issues as your data and models evolve over time. #### Try it in Colab [Create and run a simple TFX pipeline](https://colab.research.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/tfx/penguin_simple.ipynb) [Track lineage with ML Metadata](https://colab.research.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/mlmd/mlmd_tutorial.ipynb) ## Looking to expand your ML knowledge? TensorFlow is easier to use with a basic understanding of machine learning principles and core concepts. Learn and apply fundamental machine learning practices to develop your skills. ![](https://www.tensorflow.org/static/site-assets/images/marketing/cards/tile_learn_ml_promo.png) [Learn ML](https://www.tensorflow.org/resources/learn-ml) Begin with curated curriculums to improve your skills in foundational ML areas. [Learn more](https://www.tensorflow.org/resources/learn-ml) ## Get started with TensorFlow [Explore tutorials](https://www.tensorflow.org/tutorials)