How AI is Transforming Data Centres in Europe: Challenges and Considerations

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Originally posted on Datalec Precision Installations

Artificial Intelligence (AI) is rapidly reshaping industries across the globe, and its influence is undeniable in Europe, particularly in data centres. The adoption of AI technologies, such as machine learning (ML) and deep learning (DL), has brought about a wave of change in the data centre landscape. In this blog post, we will explore the high-level considerations and challenges that AI has brought to data centres across Europe.

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AI, especially when dealing with intricate deep neural networks, demands substantial computational muscle. The process of training these complex models is a resource-intensive and time-consuming task. To tackle this challenge head-on, data centres have undergone significant upgrades in their processing capabilities. This includes the deployment of more potent Central Processing Units (CPUs) and Graphics Processing Units (GPUs) to efficiently handle the computational demands imposed by AI workloads.

Real-time or near-real-time processing is a fundamental requirement for AI applications, such as autonomous vehicles, voice recognition, and video analytics. This necessitates lightning-fast data transfers between data centres and AI systems or devices. Consequently, data centres have proactively upgraded their network infrastructure to support high bandwidth and low-latency communication, aligning with the demanding nature of AI workloads. To accommodate the burgeoning demands of these workloads, data centres must possess the scalability and adaptability required. Scalability is attainable through technologies such as cloud computing and virtualization, enabling data centres to allocate resources dynamically based on AI requirements.

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To prevent overheating and maintain optimal operating conditions, data centres must ensure effective cooling systems. Moreover, the increased power consumption associated with AI infrastructure requires robust power management and efficient energy distribution systems to handle the workload requirements.

AI relies extensively on data, including sensitive or personal information, underscoring the paramount importance of stringent security measures. Data centres must enforce comprehensive security protocols to safeguard AI datasets and models from unauthorised access and breaches. Ensuring compliance with data protection regulations and addressing privacy concerns is of increasing significance as AI applications gain more widespread adoption.

The rise of AI-driven edge computing has shifted computational requirements from centralised data centres to distributed edge devices. Edge computing is well-suited for AI, offering faster processing and reduced latency when located closer to the data source. In response, data centres have adapted by deploying edge data centres or collaborating with edge computing providers to support AI workloads at the network edge.

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