Building an optimal strategy for AI infrastructure
A vitally important but often overlooked component of the infrastructure strategy for AI workloads is the need to balance cloud resources with intelligence at the “edge”—in operational environments such as warehouses, manufacturing plants, retail outlets, hospitals and roadways. Initial AI applications have focused on cloud services, but as both AI models and computational resources become thinner and lighter, the utility of AI at the edge will be essential. In fact, a recent Evans Data study found 1.4 times more inference demand is projected outside data centers versus in.
As organizations expand beyond today’s largely computer vision-based edge AI use cases to incorporate multimodal approaches using large language models, Gen AI and more, they are finding that building an effective hybrid approach utilizing both the cloud and the edge is critical to supporting revenue growth and operational efficiency. Hybrid AI combines lightweight, real-time insight at the edge with deeper context in the cloud, allowing constant interaction across various model types, latency needs and regulatory restrictions—and necessitating solutions that embrace incredibly diverse deployment environments and computational power and performance demands.
Decisions that are one or the other may result in extra cost, lost time and missed opportunities, and make it difficult and time-consuming to move beyond initial proofs of concept.
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Download NowWhy edge AI is essential
Many fundamental technical benefits can be gained from augmenting cloud infrastructure with edge technology, including reduced—or near-zero—latency, the ability to process vast amounts of data without paying to send it back and forth and data security and regulatory compliance. In addition, while cloud infrastructure can be manageable during the proof-of-concept phase, when it is scaled up and out for real-world deployment at the edge, costs can rise substantially. Leveraging industry standard x86 architecture—which is now more performant and capable of running many AI workloads—is making rollouts faster and more affordable for inference engines and customized pre-trained models.
Business value of edge AI
The technical benefits of AI at the edge are compelling, but the benefits to the business will be big enough that they will be impossible to ignore. Many businesses will come to rely on inferences in near real time. Using the edge will enable, for example, immediate changes to manufacturing processes. In healthcare, instant recognition of real-time patient data will allow for immediate decision-making and quick action that can be the difference between timely treatment and delays. Customer experiences and interactions will improve markedly with the ability to make recommendations that are immediately useful, tailor the customer journey and target customers much more directly.
Perhaps the most compelling benefit in the eyes of senior management will be the ability to build new revenue streams using intelligent systems at the edge. Retail organizations are already eyeing AI to provide recommendations based on where the customer is; inside the store, manufacturers will offer real-time customization services and new autonomous transportation/delivery services will become commonplace.
Removing complexity and using consistent infrastructure for AI at the edge enables faster deployment
AI at the edge promises to let enterprises compete and win in ways they haven’t before. Deploying what is needed to make that happen requires using a consistent and well-integrated infrastructure stack. Development and operational complexity at the edge have risen substantially with proprietary, vertically oriented technologies to date. As the edge proliferates, the differences are exacerbated and make it extremely difficult to move past pilot implementations, support numerous business units or use cases or move away from one solution to another.
The best path forward is to use a well-supported infrastructure standard that has buy-in from major industry players as well as a large group of smaller partners that offer unique capabilities. That way, a horizontal technology standard can support multiple use cases and meet the specific demands of different vertical industries.
Intel is a key partner for AI at the edge
Organizations are striving to leverage AI to the fullest. To accomplish this, utilizing AI at the edge will be mandatory as part of hybrid AI infrastructure. Deploying consistent, performant and secure edge AI infrastructure will be the hallmark of applications that deliver business success. Intel's unified cross-industry platform scales to hundreds of thousands of edge-to-cloud AI deployments by abstracting complexity from diverse computing environments, prioritizing security and manageability, and consolidating AI use cases on a platform optimized for Hybrid AI. Tens of thousands of edge AI deployments currently use Intel and partner technology at companies that include Audi, Verizon and ExxonMobil. To learn more, please click here.
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