Devopsbay | Cluster One

A Serverless AI Operating System for Data Scientists

Clusterone is a deep learning platform that makes it simple and fast to run deep learning workloads of any scale and complexity on any infrastructure. Clusterone puts data scientists first. It removes the time sink of infrastructure management and setup for data science teams by providing a ready-to-use platform and essential tools. For organizations, it reduces project costs by maximizing the efficiency of their data science teams and hardware resources.

Deep learning scientists are rare and can be difficult to recruit. So when an organisation succeeds in assembling a team of them, the prime goals should be to keep them productive and motivated. Clusterone:

Yet because their teams are small, they often get overlooked when it comes to technical help in setting up the optimal infrastructure for their work.

takes the pain and lost time out of provisioning resources, deploying clusters, creating orchestrations, and managing projects.

helps organizations maximize the efficiency of their deep learning scientists, while enhancing the satisfaction of these scientists by off-loading tedious but essential tasks.

helps organizations to get the maximum value from their hardware resources.

Clusterone’s Elastic GPU Scheduler detects unused processors and makes them available to others on the team when working with on-premises boxes. When working in the cloud, Clusterone’s dynamic scheduling and auto-scaling can spin up and spin down resources. This means scientists always have the resources their work requires without incurring billing for idle hardware instances.

“Devopsbay team likes to do everything perfectly”

The Devopsbay was Clusterone's tech partner and was responsible for tech delivery. The platform frontend, backend, cloud infrastructure and support was done by our team.The primary challenge of the platform was software orchestration using Kubernetes, cloud deployment, and automation. The platform was hosted on-premise and on AWS. Our team also deployed and supported the software for a number of on-prem customers: