"Keras is one of the key building blocks in YouTube Discovery's new modeling infrastructure. It brings a clear, consistent API and a common way of expressing modeling ideas to 8 teams across the major surfaces of YouTube recommendations."
"Keras has tremendously simplified the development workflow of Waymo's ML practitioners, with the benefits of a significantly simplified API, standardized interface and behaviors, easily shareable model building components, and highly improved debuggability."
"The best thing you can say about any software library is that the abstractions it chooses feel completely natural, such that there is zero friction between thinking about what you want to do and thinking about how you want to code it. That's exactly what you get with Keras."
"Keras allows us to prototype, research and deploy deep learning models in an intuitive and streamlined manner. The functional API makes code comprehensible and stylistic, allowing for effective knowledge transfer between scientists on my team."
"Keras has something for every user: easy customisability for the academic; out-of-the-box, performant models and pipelines for use by the industry, and readable, modular code for the student. Keras has made it very simple to quickly iterate over experiments without worrying about low-level details."
"Keras is the perfect abstraction layer to build and operationalize Deep Learning models. I've been using it since 2018 to develop and deploy models for some of the largest companies in the world [...] a combination of Keras, TensorFlow, and TFX has no rival."
"What I personally like the most about Keras (aside from its intuitive APIs), is the ease of transitioning from research to production. I can train a Keras model, convert it to TF Lite and deploy it to mobile & edge devices."
"Keras is that sweet spot where you get flexibility for research and consistency for deployment. Keras is to Deep Learning what Ubuntu is to Operating Systems."
"Keras's user-friendly design means it's easy to learn and easy to use [...] it allows for the rapid prototyping and deployment of models across a variety of platforms."
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. Keras also gives the highest priority to crafting great documentation and developer guides.
The purpose of Keras is to give an unfair advantage to any developer looking to ship Machine Learning-powered apps. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on. Your models run faster thanks to XLA compilation and Autograph optimizations, and are easier to deploy across every surface (server, mobile, browser, embedded) thanks to TF Serving, TF Lite, and TF.js.
Built on top of the TensorFlow platform, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy.
Keras is a central part of the tightly-connected TensorFlow ecosystem, covering every step of the machine learning workflow, from data management to hyperparameter training to deployment solutions.
Keras is used by CERN, NASA, NIH, and many more scientific organizations around the world (and yes, Keras is used at the LHC). Keras has the low-level flexibility to implement arbitrary research ideas while offering optional high-level convenience features to speed up experimentation cycles.