Why TensorFlow is So Popular and Tensorflow Features

Tensorflow is an open-source Machine learning framework that helps develop models, train pre-trained models by providing high level APIs. TensorFlow is an end-to-end platform to easily build and deploy Machine Learning models. TensorFlow makes it easy for novices and experts to create machine learning models for cloud, desktop, mobile, and web.

Features of TensorFlow

Let us now see the features of TensorFlow that also explains why it is widely popular

Build and Train models easily

TensorFlow offers multiple levels of abstraction to make it quick for you to choose the correct one. Build and train models by using the high-level Keras API, which makes beginning with TensorFlow and machine learning easy. Eager execution lets immediate iteration and intuitive debugging. For large ML training tasks, use the Distribution Strategy API for distributed training on different hardware configurations without changing the model definition.

TensorFlow Serving

TensorFlow Serving is a flexible and high-performance serving system for machine learning models, designed for production environments. It runs ML models at production scale on the most advanced processors in the world, including Google's custom Tensor Processing Units (TPUs).

TensorFlow Extended

TensorFlow Extended (TFX) is an end-to-end platform for deploying production Machine Learning pipelines. If you need a full production ML pipeline, use the TensorFlow Extended

TensorFlow Lite

TensorFlow Lite is a mobile library for deploying models on mobile, microcontrollers and other edge devices. For running inference on mobile and edge devices, use TensorFlow Lite.

TensorFlow.js

Train and deploy models in JavaScript environments using TensorFlow.js. TensorFlow.js is a library for machine learning in JavaScript Develop ML models in JavaScript, and use ML directly in the browser or in Node.js.

State-of-the-art models

Build and train state-of-the-art models without sacrificing speed or performance. TensorFlow gives you the control with features like the Keras Functional API and Model Subclassing API for creation of complex topologies.

Ecosystem of powerful add-on libraries

TensorFlow also supports an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT.

Robust ML Production

TensorFlow brings a direct path to production. Even if it is on servers, edge devices, or the web, TensorFlow lets you train and deploy your model easily, irrespective what language or platform you use.