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Overview

Deploy and Operate Ray as a Service in your data center and any cloud provider. Transform the way your data scientists and researchers build, deploy, and scale machine learning with Rafay’s comprehensive Ray as a Service offering for MLOps. Experience the power of Ray without the hassle of managing the underlying infrastructure and the software. This offering is purpose built for organizations looking for the flexibility to run their machine learning workloads wherever it makes the most sense, whether for cost, performance, or compliance reasons.

Ray as a Service

For each user/team, an isolated, fully functional "Ray Tenant" is provisioned on the shared host cluster.

Ray Tenant Architecture

Important

Note that every tenant has its own "Ray Head", "GCS", its own "Ray workers" and its own "Ray Dashboard"

Learn More

Learn more about Rafay's Ray as Service offering by clicking on the links shown below.

  • Design

    Learn about the design, architecture and technologies powering this.

  • Supported Environments

    Learn more about supported infrastructure and environments.

  • 👮 Administration

    Learn how you can configure, deploy and operate the multi-tenant Ray as Service offering on your Infrastructure.

  • Users

    Learn how end users can launch and use their Ray as Service tenants.