Focus on Inference: Announcing two-step model deployment and integration with NVIDIA Triton Inference Server
The Run:AI Atlas platform automates AI resource management and consumption.
Data scientists simply run jobs, with on-demand access to compute resources using any DS & MLOps tools they choose.
IT, Engineering & MLOps gain cloud-like management and control over AI infrastructure both on premises and cloud.
Business Leaders remove inefficiencies that slow down AI, increase ROI on compute, and bring AI into production fast.
It is estimated that more than 80% of AI models don't make it to production. One reason? AI requires an entirely new infrastructure stack including frameworks, software, and expensive hardware accelerators. These valuable resources are complex to manage and are often sitting idle.
Increase utilization to get better ROI from valuable accelerators
Run:ai increases the efficiency and productivity of AI. Automate AI workload orchestration and increase resource utilization by 2x. Gain full control and visibility of resources across clusters and teams regardless of location (on-premises, edge or cloud).
Simplify resource consumption and speed AI to production
Run:ai's Atlas software platform enables on-demand consumption of GPU and CPU resources across cloud and on-premises infrastructure. Researchers can choose any ML tools to manage their workloads, or use integrated Run:AI tools. MLOps can deploy models into production simply and efficiently.
Rapid AI development is what this is all about for us. What Run:ai helps us do is to move from a company doing pure research, to a company with results in production.
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