From Great Ideas to Great Results

Great ideas are no longer enough. Rapid developments in artificial intelligence (AI) and machine learning (ML) technologies have freed the minds of researchers to develop ever-more sophisticated AI initiatives. In theory. Bringing AI models to production is painfully slow, and most AI initiatives never even reach this stage. Businesses require more than just great ideas; they need the technology to realize them.

Transforming a great idea into a market-ready AI initiative involves building, training and inference. Researchers must have access to infrastructure to run experiments, scale and train workloads using different techniques and run data through AI models to calculate outputs and solve tasks. All of this requires immense volumes of data, compute power and financial resources. It also requires dedicated teams unencumbered by data, software and infrastructure management.

GPUs Have a Problem

Developments in technology and strategy have helped to create great AI initiatives from great ideas. Improving performance-and allowing businesses to get insights from data faster-can be achieved by offloading AI workloads onto graphics processing units (GPUs). Researchers are relying on this strategy and these GPUs to build and train the ML and deep learning (DL) algorithms needed to bring AI initiatives to production.

However, this strategy is also holding back the deployment of AI initiatives- and all of those great ideas! The way in which GPUs allocate resources means this process is prohibitively expensive for some organizations and doesn't deliver sufficient ROI for many more. Because of the fixed way in which resources are allocated, expensive computing power is often left sitting unused by one researcher while another waits for access.

This static method slows down the process of deploying models and hampers experimentation by researchers: a waste of both compute and brain power. Its inflexibility also puts teams at the mercy of IT, with researchers granted less visibility and control over their resources and workloads. When trying to realize AI initiatives and build applications at scale these things are critical as they help ensure compliance, security and control.

This kind of server architecture can provide the performance needed to discover business insights at the proof-of-concept stage. However, even those organizations that have the resources to deploy this approach will encounter a second problem. Static resource allocation doesn't allow for production at scale. Workload tasks are queued and completed one by one, causing inefficiencies and meaning only a small percentage of GPU resources are being used at any one time.

Great ideas will only take businesses so far. To realize these ideas, they need technology that allows them to achieve full utilization out of expensive GPUs squeeze the best out of expensive GPUs-and get the best of their research teams-while avoiding the issues long associated with using this infrastructure. Any solution must facilitate building, training and inference, without the hold-ups and inflexibility in resource allocation, which characterizes the legacy GPU model. So, what's the great idea?

We think it's a partnership between Run:ai and Dell, and the integration of Run:ai Atlas with Dell's Data Lakehouse platform. Run:ai's software streamlines the development and management of AI applications across any infrastructure: on-premises, edge or cloud. Dell's Data Lakehouse platform provides the infrastructure for Run:ai and enables a hybrid cloud environment that offers scalability and flexibility on-premises. Integration of the two means research teams can overcome the challenges associated with GPU architectures, and can build, train and conduct inferencing with speed, ease and at scale. To do this, these teams need highly performant storage to host lakehouse data. Dell PowerScale all-flash nodes and ECS/ObjectScale provide the data foundation for making this reality.

Overcoming Challenges, Optimizing Resources

Using Run:ai Atlas, research teams can gather compute and GPU resources in a centralized pool and then allocate resources across multiple workloads. Individual team members (with the required access and via secure means) can see how resources are being used, with visibility into other workloads, users, projects and departments easily granted and controlled.

Resources can be allocated and managed automatically, reducing the heavy lifting with which team members are tasked. Instead of having to hire new talent or force researchers to wear multiple hats, the collaboration between Dell and Run:ai introduces a new, invisible team member: an AI expert operating behind the scenes to optimize resource allocation. One company-a specialist in facial recognition technologies-went from 28% GPU utilization to optimization of over 70%. By streamlining GPU utilization, the team could build faster and could budget better with more visibility.

Dell's Data Lakehouse is also helping businesses overcome challenges inherent in managing data and mining it for valuable insights. The hybrid cloud environment gives teams self-service access to balance workloads between IT locations and manage sensitive data securely, without needing to send it to the cloud. Encompassing compute, storage and software components in a single, secure platform means teams can focus on realizing great ideas without wasting time managing infrastructure and worrying about data governance and safety.

Accelerating the Journey to AI Greatness

The partnership and integration of technologies is helping more businesses overcome common problems in scheduling GPUs. It's reducing-and eliminating-friction and challenges across every stage of AI development: from experimentation and proof of concept to deployment. Great ideas are being turned into great AI initiatives that hit the market faster, enable better business insights and deliver all-important ROI. Fostering greater collaboration and reducing workload, researchers are freed up to focus on developing their ideas and helping accelerate their team's AI journey.

For a deeper dive into how Run:ai Atlas and Dell's data lakehouse platform can optimize your AI projects, check out this whitepaper.

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