run:ai

The execution of professional AI projects places the highest demands on the underlying IT environment. Data Scientists who set up and train Deep Learning systems often use GPUs to get the computing power needed. These GPUs are usually statically allocated to the number of users. This results in some GPUs waiting unused at some times while other Data Scientists wait for more GPUs to train their algorithms. 

run:ai has developed the world's first orchestration platform for AI computing. By separating workloads from the underlying hardware, run:ai creates a shared pool of GPU resources that can be dynamically provisioned, enabling efficient orchestration of AI workloads and optimized use of GPUs. Data Scientists can seamlessly leverage large amounts of GPU power to improve and accelerate their research, while IT teams maintain centralized, cross-site control and real-time visibility into resource provisioning, queuing, and utilization. The run:ai platform is built on Kubernetes and enables easy integration with existing IT and data science workflows.

By using run:ai's resource pooling, queuing, and prioritization mechanisms, Data Scientists are relieved of the challenges of infrastructure management and can focus solely on data science. They can run as many workloads as they need to without experiencing computational bottlenecks. run:ai's fairness algorithms ensure that all users and teams receive their fair share of resources. Policies for priority, or prioritized projects, can be set in advance, and the platform allows resources to be dynamically allocated from one user/team to another, ensuring that all users get timely access to coveted GPU resources. The run:ai scheduler allows users to use fractions of a GPU, entire GPUs, or GPUs with multiple nodes for distributed training on Kubernetes. This way, AI workloads are run on demand rather than by capacity. Data science teams can run more AI experiments on the same infrastructure.

run:ai helps enterprises simplify and accelerate their AI journey from start to finish. With a multi- and hybrid-cloud platform (run:ai Atlas) built on a cloud-native operating system, Atlas supports users' AI initiatives anywhere (on-prem, at the network edge, in the cloud). The pooling of all compute resources and the efficient and automated management of those resources enable IT departments to offer AI-as-a-Service and move from reacting to AI to accelerating AI.

Acceleration with MLOps: Enable MLOps and AI engineering teams to rapidly operationalize AI pipelines at scale, run production machine learning models anywhere using the built-in ML toolset or by easily integrating their existing third-party toolset (MLflow, KubeFlow, etc.)