Easy and quick Distributed Computing
Develop any application at any scale.
Run the same code on your laptop, on a powerful multi-core machine, on any cloud provider, or on a Kubernetes cluster.
Utilize machine that is scalable libraries from the box for hyperparameter search, reinforcement learning, training, serving, and much more.
Ray is just a distributed execution framework that allows you to scale your applications and to leverage state of the art machine learning libraries.
Powered by Ray
вЂњAnt Financial has generated a multi-paradigm fusion motor along with Ray that combines streaming, graph processing, and machine learning in a single system to do real-time fraud detection and promotion that is online. RayвЂ™s freedom, scalability and efficiency allowed us to process huge amounts of dollars well worth of transactions during Double 11, the largest shopping time in the world.вЂќ
вЂњAt ASAPP, we try out machine learning models each day through our open source framework FlambГ©, and now we ultimately deploy a lot of those models to manufacturing where they provide an incredible number of real time customer interactions. We tried using more generic task distribution frameworks but they didnвЂ™t fit our needs until we found Ray. Using Ray has allowed us to quickly and reliably implement ML that is new t ling scale, and stepped on big groups of devices efficiently, enabling FlambГ© to develop and help our model training for both research and production.вЂќ
вЂњEricsson makes use of Ray to create distributed reinforcement systems that are learning interact with community nodes and simulators with RLlib also to tune device learning models hyper-parameters with Ray tune.вЂќ
вЂњCreating individualized device (chip) screening to reduce test cost, improve quality while increasing capacity for Intel manufacturing and testing process.