AI model development is a complex and challenging process that not only requires specialized skills and expertise in data science but also often requires significant compute resources, such as hardware and time. Unlike traditional software development, which often follows a linear, step-by-step process, AI model development is iterative and experimental. Developers often evaluate different algorithms, architectures, and training techniques to see what works best for a particular task or dataset. This process makes established IT tools for tasks such as capacity planning and quota management largely irrelevant. As a result, IT and MLOps teams often find themselves with few controls and limited visibility into compute resource allocation and use. Data scientists might be limited to static GPU allocation or must wait for specific GPU resources to become available for use, while other available resources across the organization stand idle.

The Run:ai Atlas software platform can streamline AI development, training, and deployment by orchestrating GPU resources. By abstracting the underlying GPU infrastructure, Run:ai Atlas optimizes use of AI clusters by enabling flexible pooling and sharing resources between users, teams, and projects. The software distributes workloads in an elastic manner - dynamically, without needing to reboot, changing the number of resources allocated to a job - allowing data science teams to run more experiments on the same hardware. IT teams retain control by setting automated policies for resource allocation and gain real-time visibility, including run-time, queueing, and GPU utilization of each job. A centralized dashboard displays projects and jobs across multiple sites, whether on premises or in the cloud. The Run:ai Atlas platform is built on Kubernetes, enabling simple integration with existing IT and data science workflows.