What is Tensorflow Used For? We Surveyed the Professionals

Initially the software known as TensorFlow was an in-house project spearheaded by the Google Brain team for internal Google use in research and production. The first public version was released under the Apache License 2.0 in 2015.

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The Rise of the 'Stack Developer'

Google released the updated version of TensorFlow, named TensorFlow 2.0, in September 2019. This release helped to power the rise of the 'Stack Developer' in AI research. Stack Developers were already versed in server, and client side web development. Tensorflow is a cornerstone of large language model(LLM) based AI deployments. One example is text-to-image AI called Stable Diffusion. Stable diffusion is a new Generative AI tool.

TensorFlow 2.0 is a flexible and powerful server based platform that provides a comprehensive ecosystem of tools for developers, enterprises, and researchers. Cutting edge developers who want to push the envelope of state-of-the-art in machine learning by building scalable ML-powered applications. TensorFlow 2.0 has made its basic use as familiar as possible for the Python development community.
With tight integration of Keras into TensorFlow, eager execution by default, as well as Pythonic function execution,

TensorFlow 2.0 supports deployment to any platform and offers many performance improvements on GPUs. It allows you to run models with TensorFlow, deploy them with TensorFlow Serving, use them on mobile and embedded systems with TensorFlow Lite, and train and run in the browser or Node.js with TensorFlow.js.

Tensorflow Facilitates Development and Deployment of Machine Learning Models

In addition to what we mentioned above, TensorFlow 2.0 also offers several features to facilitate the development and deployment of machine learning models. The SavedModel file format undoubtedly provides a standardized way of running models on various runtimes, including the cloud, web, browser, Node.js, mobile, and embedded systems.

To help reduce server overhead TensorFlow 2.0 is currently providing a Distribution Strategy API that supports distributed training. This is training with minimal code changes and it provides excellent out-of-the-box performance. Multi-GPU support is available, and Cloud TPU support is coming in a future release.

What is Tensorflow Used For TensorFlow 2.0 Serving stack python google AI

Significant Improvements In Performance On GPUs

It must be remembered that TensorFlow 2.0 has also made significant improvements in performance on GPUs. Up to 3x faster training performance when using mixed precision on Volta and Turing GPUs, with a few lines of code. TensorFlow 2.0 is tightly integrated with TensorRT and showcases an improved API to deliver better usability coupled with higher performance.

Overall, TensorFlow is used for a robust and flexible server platform that empowers developers, enterprises, and researchers to build and deploy machine learning applications easily and efficiently. The platform was started by Google Brain out of necessity. TensorFlow is constantly evolving. New features and improvements are being added regularly to support the growing demands of the machine learning community. Keep in mind that many Deep Learning Language models rely on software like TensorFlow to operate.

Tensorflow Projects? We Surveyed the Professionals.

Basically TensorFlow 2.0 is a flexible and powerful platform that provides a comprehensive ecosystem of tools for developers, enterprises, and researchers who want to push the state-of-the-art in machine learning and build scalable ML-powered applications. With tight integration of Keras into TensorFlow, eager execution by default, and Pythonic function execution, TensorFlow 2.0 makes the experience of developing applications as familiar as possible for Python developers.
TensorFlow 2.0 supports deployment to any platform and offers many performance improvements on GPUs. This software allows you to run models with TensorFlow while simultaneously deploying them with TensorFlow Serving. The latest update lets you use it on mobile and embedded systems with TensorFlow Lite. Features include the ability for you to train and run TF in the browser or Node.js with TensorFlow Java script.

What is Tensorflow Used For - Key Features:

Tight integration of Keras into TensorFlow
Eager execution by default
Pythonic function execution
Performance improvements on GPUs
Run models with TensorFlow
Deploy models with TensorFlow Serving
Use on mobile and embedded systems with TensorFlow Lite
Train and run in the browser or Node.js with TensorFlow.js
SavedModel file format for standardized model runtime
Distribution Strategy API for distributed training with out-of-the-box performance
Multi-GPU support
Cloud TPU support (coming soon in a future release)
Improved performance on GPUs with up to 3x faster

Flexible Software Development Platform that Empowers Developers, Enterprises, and Researchers

Overall, TensorFlow 2.0 is a robust and flexible platform that empowers developers, enterprises, and researchers. Researches using TensorFlow can build and deploy machine learning applications easily and efficiently. In the final analysis this platform is constantly evolving. New features and improvements are being added regularly. New features that are targeted to help support the growing demands of the machine learning community.

If you want rto learn more about what TensorFlow is used for:

See the whole technical paper released by Google Brain team: Mart´ın Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur,
Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker,
Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng.