Waterloo Data’s Solution as a Service (SolaaS) mitigates heavy investments in MLOps personnel
Austin, TX – September 20, 2023 – Today, Waterloo Data announces the immediate availability of GpuGlue, a simple but elegant solution for dramatically lowering the barrier to entry for high performance computing. This solution boot-straps an Ubuntu server distribution with Docker, CUDA, Nvidia Container Toolkit, Kubernetes, and Kubeflow through a custom Ubuntu repository. The packaged solution addresses the common pain points faced by teams when setting up AI/ML ops independent of GPU acceleration requirements.
The business world is awash in more data than ever before, with projections estimating that 90% of all the world’s data was created in the last two years alone. As a result, the race to capitalize on available data – to disrupt competitors and avoid being disrupted – has ramped tremendously. With 2023’s mass sprint towards AI technologies, enterprises are struggling to keep pace with the ML Operations (MLOps) necessary to support them. In addition to a severe shortage of MLOps talent, Gartner reports ML infrastructure is sorely lacking, saying: “86% of organizations identified at least one of the following areas as a weak link in their AI infrastructure stack: GPU processing, CPU processing, data storage, networking, resource sharing, or integrated development environments.” IDC concurs saying: “our research consistently shows that inadequate or lack of purpose-built infrastructure capabilities are often the cause of AI projects failing.” Finally, Deloitte calls out the challenge and solution clearly, writing: “the traditional computing infrastructure used for standard enterprise applications is just not enough for large-scale AI,” and specifying that GPU-accelerated Artificial Intelligence is the secret to rapid and insightful AI.
“Our clients are increasingly aware that succeeding with AI requires vast computational power and they’ve begun expressing concern that traditional compute resources – even in hyperscale cloud environments – are simply too expensive at scale,” said Matt Brown, CEO Waterloo Data. We developed GpuGlue to help enterprises capitalize on GPU-accelerated computing, getting started quickly and easily – without deep, on-staff MLOps expertise.”
Available now at no cost, GpuGlue enables enterprises to quickly begin using GPU-accelerated computing for model AI model creation and deployment, AI model training and improved AI model accuracy – all at the lowest possible cost for available compute power. This best-of-breed MLOps toolkit has been designed to automate the creation of GPU-accelerated AI environments, and has been carefully curated and extensively tested on GPU core systems in Waterloo Data’s advanced data science lab and across Waterloo Data’s private virtual mesh cloud.
Further underscoring challenges faced by enterprises, Luke Stamm, Waterloo Data CTO, stresses the need to get GPU-accelerated AI platforms configured properly from the start. “As folks know, once all of this is set-up incorrectly, it becomes a support nightmare. Our solution ensures this is done correctly out of the box, allowing data scientists and MLOps teams to focus on the actual work and not fiddle with tedious and frustrating configuration tasks.”
For more information visit : https://waterloodata.com/gpuglue
About Waterloo Data
Since 2009 Waterloo Data has helped organizations solve their most complex data management, cloud infrastructure, and outsourced product development challenges. Headquartered in Austin with offices throughout Texas and a nearshore delivery center in Monterrey, Mexico, Waterloo Data provides enterprise management and technology consulting services for clients in Healthcare, Financial Services, Commercial Real Estate, and high growth technology companies.
Press Inquiries: Jamey Heinze, CMO, Waterloo Data – firstname.lastname@example.org