Enterprises are beginning to harness AI to build unique value and business outcomes will be at the centre of an enterprise’s AI strategy. Hybrid IT enables these organisations to be agile and scalable, which in turn, allows them to grow.
Data will have immense value, too. AI use cases highlight this fact, not only for the insights AI can extract, but also for how a strategic approach to data management is directly tied to its ability to create value. This includes location, proximity to processing and users, and the ecosystem in which AI operates. The success of AI deployments depends on the physical infrastructure that provides the high-performance cooling, layout, and connectivity required for AI to operate effectively.
"What is clear is that data centres are a key component of AI enablement" Jan Hnizdo, CEO, Teraco wrote in a recent blog. He said that Ecosystem-rich data centres, with seamless movement of data via interconnectivity, are set to become the centres for data exchange, playing a vital role in the future of AI and data-intensive applications. Enterprises need purpose-built infrastructure and the right partners to fuel innovation today and well into the future. Here’s why.
Key to success: proximity to data, users, and ecosystems
For AI to reach its full potential, deployment in a location that offers proximity to data sources, users, and the broader ecosystem in which an enterprise operates will be crucial. AI relies on large datasets that can reside across multiple cloud platforms, enterprise databases, and IoT devices at the edge. The closer the AI deployment is to these data sources, the more efficient and effective it will be.
Importance of location
AI applications are highly latency sensitive. This means that reducing latency is crucial for optimal performance. AI models often use vast datasets from diverse sources. Therefore, deploying AI in facilities that boast dense ecosystems of high-speed, secure networks is key to minimising latency and ensuring efficient data exchange.
To optimise AI, it is vital that enterprises collocate at data convergence points that leverage the combined strengths of cloud, AI, and network ecosystems. Such integration of proprietary data repositories is crucial for seamless data flow and processing, which in turn, enhances AI performance.
Role of inference in AI
Inference is the process where trained AI models are used to make decisions on new, unseen data. It involves taking input data, applying learned patterns and relationships, and generating output predictions or decisions. For optimal performance, inference must be close to data sources. Enterprises are increasingly deploying AI models closer to their data ecosystems to improve latency, reduce costs, and enhance overall performance.
During the inference phase, AI models are deployed to make real-time predictions or decisions based on new data. This phase needs to be close to the data sources or the edge. Processing data close to the edge allows for immediate issue resolution. This proximity reduces latency and improves AI performance.
AI workflows involve three interdependent stages: data aggregation, training, and inference. Each stage demands different technological support. The massive amounts of data required for training and deep learning are computationally intensive, necessitating specialised hardware, as well as robust power and cooling solutions.
Power of interconnection
Robust interconnections within cloud environments are essential to reducing costs and minimising latency. Interconnection involves multiple carriers, clouds, content providers, enterprises, and application service providers connecting directly at edge routers or switches on each network. As AI accelerates enterprises’ interconnection strategies, the location of on-premise systems become increasingly important.
Rise of hybrid and private cloud deployments
While public cloud adoption has become widespread, many enterprises still find it too expensive to run all their data in the cloud. Consequently, they often opt for a hybrid model, which means running certain services in their own private cloud facilities. These environments, which typically feature high-density racks, are being deployed in highly available, ecosystem-rich data centres with access to networks and major public cloud on-ramps.
Enterprises are likely to follow a similar approach to AI, with their unique data residing in either their private or public cloud and similarly with AI processing. Considering where the enterprise’s data resides will be key. For many enterprises, this means embracing a hybrid AI model within colocation data centre environments built to meet AI’s power and cooling demands.
Cooling considerations
High-density cooling is critical in managing AI deployments within ecosystem-rich data centres. These centres must be equipped to handle the intensive power and cooling requirements of AI workloads, ensuring that data flows efficiently through interconnected systems. This capability makes them central to data exchange.
Teraco’s ecosystem-rich data centres will increasingly play a role as the central hubs for data exchange in the AI-driven world. By providing the necessary proximity to data, users, and ecosystems, these facilities enable efficient AI deployment and operation. As enterprises continue to integrate AI into their operations, enterprises should consider collocating at data convergence points so that cloud, AI, and network ecosystems can be leveraged to their full potential.
