TEL AVIV, Israel, July 8, 2020 /PRNewswire/ — Run:AI, a company virtualizing AI infrastructure, today announced that it is working with the London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare as a technology provider to help them better manage their AI resources and provide elastic resource allocation, visibility and control.
The AI Centre, led by King’s College London and based in St Thomas’ Hospital, uses an enormous trove of de-identified patient data held by the NHS, including medical images and patient clinical pathway data, to train sophisticated AI learning algorithms. These algorithms are used to create new tools for faster diagnosis, personalized therapies, and more effective screening.
Established by the UK Government’s Industrial Strategy Challenge Fund, the AI Centre also includes Imperial College London, Queen Mary University London, four NHS trusts and a number of industry partners. Since the outbreak of the Covid-19 pandemic, the AI Centre has devoted much of its resources to the fight against the novel coronavirus. It recently contributed an AI diagnostic tool that found anosmia (losing the sense of taste and smell) to be a stronger predictor of COVID-19 infection than fever, and resulted in the UK Government amending its official advice on suspected infections.
Run:AI ensures that the AI Centre’s data scientists can get the full use out of their hardware, guaranteeing that GPU (Graphics Processing Unit) resources are efficiently and elastically allocated to teams that need them. This enables the AI Centre to run more experiments and to speed up time to results, while providing cross-team visibility into how their hardware is being used.
Since installing Run:AI, the AI Centre has slashed the time taken to complete its experiments. The current average is just a day and a half, whereas a simulation of the AI Centre’s exact infrastructure running without Run:AI showed an average of over 46 days per experiment – an improvement of 3000%. Over a 40-day period, the researchers ran more than 300 experiments after installing Run:AI compared to just 162 in a simulation of the same environment over the same time period. In addition, actual GPU utilization increased by 2x in the months since Run:AI’s platform has been in use.
“Our experiments can take days or minutes, using a trickle of computing power or a whole cluster,” said Dr. M. Jorge Cardoso, Associate Professor & Senior Lecturer in AI at King’s College London and CTO of the AI Centre. “With Run:AI we’ve seen great improvements in speed of experimentation and GPU hardware utilization. Reducing time to results ensures we can ask and answer more critical questions about people’s health and lives.”
“Healthcare is one of the most important and impactful uses of advanced AI, especially now as it can help save lives during the Covid-19 pandemic. We’re proud to be working with the London AI Centre to help ensure their important research can get the best use out of their hardware, so they can run more experiments quickly and efficiently,” said Omri Geller, CEO and co-founder of Run:AI.
About the London Medical Imaging & AI Centre for Value Based Healthcare
The London Artificial Intelligence Centre for Value Based Healthcare is one of five Centres of Excellence, established as part of the UK Government’s Industrial Strategy Challenge Fund, delivered through UK Research and Innovation. Its core purpose is to drive health and economic benefit by making NHS clinical data accessible for artificial intelligence (AI)-driven research and development, and to support the deployment of the resulting products across the NHS. Led by King’s College London and based at St Thomas’ Hospital, the Centre brings together an ambitious consortium of partners including Imperial College London, Queen Mary University, four NHS Trusts, major industry partners including NVIDIA, Siemens Healthineers, IBM and GSK, and a growing cohort of small-medium enterprises in the UK.
Run:AI has built the world’s first orchestration and virtualization platform for AI infrastructure. By abstracting workloads from underlying hardware, Run:AI creates a shared pool of resources that can be dynamically provisioned, enabling efficient orchestration of AI workloads and optimized utilization of expensive GPUs. Data Scientists can seamlessly consume massive amounts of GPU power to improve and accelerate their research while IT teams retain centralized, cross-site control and real-time visibility over resource provisioning, queuing, and utilization – whether on premises or in the cloud. The Run:AI platform is built on top of Kubernetes, enabling simple integration with existing IT and data science workflows.