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2023 – The year to deploy Machine Learning and AI in your data center?

Need to support real-time optimization.

Unfortunately, while traditional data center infrastructure toolsets may have very solid use cases, they’re less flexible when it comes to directly supporting real-time optimization activities. BMS is of course a key platform, but it typically doesn’t feature any analytics and is usually designed to alert only on hard faults or SLA breaches – which is often too late to prevent outages. Similarly, Electrical Power Management Systems are often treated as a BMS extension.

While CFD systems can be great to support new build projects or major design changes, they typically only utilize data from a specific point in time under set parameters – making it overly complex and expensive to unlock real-time optimization opportunities. DCIM systems too tend to be largely driven out of IT requirements, with very little focus on the M&E side. Because many DCIM vendors originated from the IT side of the fence and, although they might have claimed comprehensive functionality for ‘inside the rack’, none of them have yet to properly address the very real M&E needs of data center operators – particularly when it comes to overall energy efficiency and capacity management.

Download 2023 – The year to deploy Machine Learning and AI in your data center? Whitepaper

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