Organizations investing in AI are reaping huge benefits, with 63% reporting revenue increases in business units that incorporated AI. Those investing in AI at scale are three times more likely to report revenue gains of 10% or more. For many organizations however, scaling AI can be a source of anxiety, with 84% of executives fearing they’ll miss a growth objective if they don’t succeed.
That’s why Interxion: A Digital Realty Company, Nvidia, and Core Scientific came together to create a clear path to enterprise AI. In a recent webinar, Accelerating Business Innovation and ROI with AI Platform-as-a-Service, experts from each company outlined the ways IT infrastructure drives AI innovation. The panelists were:
- Tony Paikeday, director of product marketing for artificial intelligence and deep learning at NVIDIA
- Ian Ferreira, chief product officer for AI at Core Scientific
- Wes Jensen, director of global technology business development at Digital Realty
MLOps can industrialize AI
AI models are not developed or deployed in the same ways as conventional business software, which can make the process daunting and inconsistent, Paikeday said. Due to a lack of AI-specific resources, both human and infrastructure, many organizations are incurring a growing amount of AI model debt, or shadow AI – under-deployed AI models that yield low ROI.
Why? For one, data scientists are not trained engineers – they don’t always follow great DevOps practices. That’s where engineers and IT should come in, but unfortunately these teams often operate in silos. Second, there’s a myriad of choices for machine learning (ML) tools and platforms, which stunts process standardization.
If enterprises want to successfully scale AI to its maximum potential, the AI development cycle needs to shift from an artistic process to an industrial one. Paikeday said the answer is an IT-approved platform that combines data science and IT DevOps in a streamlined, manageable process – MLOps.
MLOps includes AI-ready hardware that delivers the necessary resources for every job along the development cycle, ultimately reducing the time to push models into production, decrease friction between teams, improve model tracking, versioning, monitoring and management. The result? A cyclical lifecycle in which models can eventually be monitored and retained.
Empowering data scientists with infrastructure
From start to finish, the process of adopting AI for your business begins with data. Data scientists are taking on some of the world’s toughest challenges. They need the right tools and resources to be successful.
That’s where data centres come in, Jensen said. Digital Realty’s PlatformDIGITAL™, available across all Interxion data centres, is AI infrastructure reimagined. The single global data centre platform enables AI frameworks and workloads, enhances your enterprises’ AI capabilities and offers limitless density and access. Jensen detailed the solutions that, when combined, allow enterprises to support any workload or job size with any node at any time:
Once enterprises have cutting-edge hardware specifically designed for the challenges AI poses, paired with the ease of public cloud offered through colocation, they need a best-in-class software stack to streamline workflows. Ferreria listed what this software stack, like that of Core Scientific, looks like:
- Comprehensive DCG-ready software solutions
- Simplified large-scale multi-node workloads
- Support for “burst” compute
- The ability to optimize for cost and performance
- A single pane of glass for data scientists
Enterprises need the right infrastructure to translate data scientists’ innovative breakthroughs to a profitable competitive edge. From compute to connection and colocation, to the right software stack, many moving parts need to fit together to standardize the development process for deployment that solves problems and returns a positive ROI.
To learn more about constructing your AI infrastructure with Interxion: A Digital Realty Company, Nvidia and Core Scientific, watch the full webinar.