ClearML, an integrated MLOps platform, has launched new capabilities for managing and scheduling GPU compute resources, regardless of whether the application is deployed on-premise, in the cloud, or via hybrid.

Its key features include an Enterprise Cost Management Center that shows DevOps and ML engineers everything that is related to GPU clusters, allowing them a better approach to managing job scheduling, fractional GPU allocation and utilization, and determining project quotas.

In addition, the company is set to release new web app capabilities that will allow data scientists to build up a safe Jupyter Lab or VS Code IDE right in a secure ClearML UI browser window, making it easier for data scientists to work remotely securely and seamlessly. 

Established in 2016 and based in Israel, ClearML provides an open-source platform that supports the entire ML lifecycle. Currently, the platform is utilized by over 1,300 enterprises, encompassing 150,000 data scientists.