On-demand webinar with Microsoft & Github: Git-Based CI / CD for Machine Learning & MLOps
GPUs play an important role in improving performance and scalability. However, GPUs are commonly under-utilized due to inefficient resource allocation, data bottlenecks, complicated DevOps and limited support for use-cases beyond deep learning. In a cloud-native era, data scientists need a GPU-powered machine learning PaaS based on open source technologies, with lower costs and/or on-premises.
GPUs sit idle while data scientists develop code, and when they are eventually needed there isn’t enough of them. As a result, heavy expenses are spent on unutilized GPUs, while not enough GPUs are used when running jobs. Iguazio provides a managed shared pool of resources and auto-scales GPUs as needed for maximum utilization.
Iguazio provides a fully managed platform with pre-installed services, built-in GPU enablement and serverless automation, all managed with a friendly UI. This cuts months spent on DevOps, enabling developers to focus on code as opposed to plumbing.
Typical data intensive applications require 10s or 100s of terabytes and lack of appropriate integration blocks data scientists from enjoying the benefits of GPUs. Iguazio streams data in chunks directly into GPUs, using SSDs at in-memory speeds and low latency access.
Many businesses use GPUs at the edge to avoid the cloud’s heavy costs, but suffer from a poor user experience. Iguazio’s GPU as a Service runs in multicloud, edge and on-premises environments, providing a friendly cloud experience anywhere.
“Iguazio complements the compute power of GPUs by directly connecting GPUs to large-scale, shared data infrastructure.”
Director of Product