The computing edge is getting more powerful. From the smartphone in our pocket, to the desktop (laptop) devices we use every day, to the check-in “kiosk” computers at airports, and the digitally linked All machines are getting smarter, right down to traffic lights. As you know, much of this intelligence is now divided into artificial intelligence (predictive, reactive, and of course generative) and artificial intelligence that processes information through pattern recognition processes that allow us to make our lives better. is driven by the ability of
Users’ broader notion of AI in these scenarios is that the intelligence happens on the device itself and through the juice pipes that cloud data centers provide on the backend, but that critical data assets reside in more remote entities. Internet of Things (IoT). ) The device is given enough edge computing engine power to run a significant degree of its own algorithmic intelligence.
Industry 4.0 requirements
Often considered the birth of so-called Industry 4.0 (steam, electrified production, and computing being the first three), analyst firm McKinsey defines this period in our evolution as: . Disruptive trends include the rise of data and connectivity, improvements in analytics, human-machine interaction, and robotics. ” That’s fine, but you can’t just plug these machines in and expect them to work. We need a new approach to high-bandwidth data services that provides seamless connectivity. It also runs on secure, low-latency connections, even when it’s on the compute edge.
NTT Data and Schneider Electric have a reasonably balanced history in this field going back several years and are looking to create new We have been working on data fusion collaborative innovation initiatives. Build smart factories, smart cities, and do the next big thing in smartness that will inevitably come next.
Gen-AI at the edge
The companies announced a new collaboration initiative aimed at establishing connectivity, an initiative designed to integrate edge computing assets and devices with private 5G technology, IoT, and modular data centers. It is also intended to support the computational demands of generative AI applications deployed in enterprises. corner. For the sake of completeness, we define a modular data center (MDC) as a small data center, typically in a shipping container or prefabricated building, with hardware and software components designed and engineered to: Let’s define it as a way to construct something (often housed in something similar). , modular, meaning they can be added or subtracted more easily depending on the equipment needs of any facility.
Schneider Electric, known for its work in digital technology and energy management and automation, is a partner of NTT Data’s Edge-as-a-Service (literally, edge computing service for IoT devices, but managed, controllable & automated). (provided as a service). Supported services include fully managed edge-to-cloud, private 5G, and IoT capabilities. The convergence point is where NTT DATA’s Edge-as-a-Service meets Schneider Electric’s EcoStruxure. EcoStruxure is a modular data center that combines operational technology (OT – hardware and software that monitors devices, processes, and infrastructure for data processing and networking reasons) with other technologies.
Differences in AI inference
What all this brings to Industry 4.0 deployments is power, but this is a specific type of power designed to handle compute-intensive tasks at the edge such as machine vision, predictive maintenance, and other AI inference applications. is.
“We listen to our customers and know that the future of digital transformation lies in processing the vast amounts of data generated by edge devices. That’s why we are developing solutions that address these obstacles. We are excited to announce that we are ready to lead the way towards a more connected and efficient digital world. We are excited about the prospect of what this will mean,” said Shahid Ahmed, Vice President of New Business and Innovation at NTT Ltd.
This technology integration enables the deployment of edge data centers for remote and brownfield locations with advanced computing needs that require infrastructure such as power, cooling, racking, and dedicated IoT and AI management systems. is designed to enable. As discussed here earlier, brownfield sites are already muddy. This means that while there is some form of existing infrastructure to build on, the deployment is being done in a partially plowed field where legacy data migration tasks, various integration tasks, and new seeds (software) still exist. I will. code).
As enterprises look to use edge computing to support automation and enable data-driven decision-making, NTT Data finds that nearly 70% of enterprises are deploying edge to solve critical business challenges. It points to an Edge Advantage report that suggests it’s accelerating. The first private 5G-enabled deployment of EcoStruxure data centers is located in Marienpark, a more than 74-acre innovation zone in Berlin.
“After leveraging NTT Data’s expertise in private 5G connectivity and maximizing synergies with the EcoStruxure architecture within our facilities, it’s time to expand our collaboration and offer a complete solution to our industrial customers.” Schneider Electric. “Together, we are helping clients around the world deploy the right edge computing infrastructure with connected devices, specialized industrial solutions and modular data centers, especially as their IoT and emerging AI requirements evolve. We aim to help you gain valuable data insights in
Building a stronger edge (computing)
Camille Mendler, Principal Analyst, Enterprise Services, Omdia, reminds us that AI-enhanced data already accounts for a third of enterprise network traffic. “But in the future, digital interactions will become mainstream by 2030,” Mendler said. “To benefit from AI insights, companies must now invest in digital resources and supporting technology infrastructure at the edge. , relies on actionable data-driven intelligence delivered in real-time,” she said.
While there’s a lot of deep tech infrastructure and network backbone technology involved here, what’s happening here is simple. We need to take a closer look at edge computing (that is, computation, analysis, and data management processes that occur externally on the Internet of Things). Power.
No, a robotic arm won’t suddenly start thinking for itself, but in fact, to some extent it will…know what’s happening next on the production line, whether maintenance is needed, and when it’s going to take time. Because you start using generative AI capabilities to understand. It’s tea break. Well, robots don’t go to tea (or coffee breaks), but you get the point. The explosion of AI applications will require more power at the computing periphery, and the periphery of these devices will itself move closer to the core enterprise applications. And stack them.
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