Machine learning (ML) is one of the applications of artificial intelligence (AI) that is increasingly present in everyday life, and it has great potential, as it is one of the main pillars on which digital transformation will be based.
Its use is growing exponentially and, according to the media specialized in technology, it is estimated that it will increase the efficiency and sustainability of the data center sector by up to 40 percent in the not-too-distant future.
Do you think that AI is already a widespread reality in data centers?
Artificial intelligence is all the rage. Everything now, to be cool, must have artificial intelligence in some way, and data centers and critical infrastructures are no exception.
Often included among the so-called "intelligent systems" are rule-based systems, where there is no value in the data, but in the rules, there is no learning, they do not scale well, and they do not behave well in anomalous situations. This is nothing more than experience or knowledge bottled up in an application. But it is not enough to call it AI.
So, how should AI be applied in a data center?
Artificial intelligence and machine learning tools must maximize the visibility of the operation in the environment of technical rooms and data centers, ultimately facilitating better decision making and automation, by placing data analytics at the heart of operations.
The use of a combination of machine learning techniques and rule-based systems, that is continually evolving to maximize the value that data analysis, brings to operators is the key for a comprehensive AI tool.
I don't really believe in rules-based systems, but best practice guidelines and standards impose thresholds and rules on us, for that reason, it is critical to combine them with machine learning techniques capturing unprecedented amounts of data so that the results are robust to anomalies and can scale reasonably.
Finally, what does ML and AI bring to the data center?
Artificial intelligence and machine learning tools maximize the visibility of the operation in the environment of technical rooms and data centers, ultimately facilitating better decision making and automation.
By placing data analytics at the heart of operations, ML & AI deliver energy savings, increased robustness, and operational efficiency. Providing the information operators need to transform their business.
Having the right data helps businesses across the board make better decisions, making it easier to improve productivity and energy efficiency. Understanding data, analysing it intelligently, and identifying patterns makes it easier to develop business strategies that fit different needs, substantially reducing errors and optimizing resources.
Leveraging AI in the data center in a viable way is necessary for all data-driven businesses. Gartner has stated that more than 30 percent of data centers that do not implement AI and machine learning will not be operationally and economically viable soon. Therefore, all data-driven businesses must implement AI and true machine learning in their data centers. AI will also help organisations stay ahead of growing data storage and processing needs.
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