AI for Advanced Data Center Analytics: Here's What to Know

By CIOReview | Wednesday, November 21, 2018

Today’s companies want to implement Artificial Intelligence (AI) and Machine Learning (ML), as they want more comprehensive analytics strategies to achieve their goals. The need for computing to handle many instructions per second has increased. AI applications are developed to process the information quickly, and they need immediate and reliable connectivity. The demand for high data centers is the data-intensive factor of AI applications. The data centers can provide the support that can handle today’s complex workloads. Companies which involve customer engagement use AI and ML to analyze the conversations of customers that ultimately lead to a transaction or even predict when a transaction occurs.

Companies need reliable connectivity and proximity to AI-as-a- Service resources which are made available for the customers. Cloud providers like Google and Amazon, have started offering AI-as-a- Service solutions for those organizations that to excel in their AI capabilities. Companies that use AI-as-a-service need immediate and reliable connectivity that can only be possible by data centers in centralized hubs. Data present in the hub can be distributed to users, applications, and business units. The data hub enables centralized control of data to be made possible for compliance and security, and self- service access to data for user productivity.

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Companies should opt for the technologies that can mix and match various workloads in a single space. Workloads can be of different in size and power, speed to operate. Data center operator should allow for workloads that can be shifted easily and prioritized when required. AI applications have multiple moving parts with varying capacity needs that have to be moved based on the application's action and activity. With the many subcomponents involved, a rack that can consume 5KW at one point can ramp up to 35KW for seven in an application. If these racks have to be positioned into one “pod,” a single contaminated zone that can make the managers shift data easily instead moving racks consecutively.

Companies show interests in the applications which have more than usual components. Applications that depend on a large number of CPUs can consume more power and energy, which cannot be cooled by the regular methods. Organizations look for data centers that offer multiple cooling points like unique contaminant solutions, water chilled racks which effectively manage heat dissipation.

The technologies like AI, ML have the advantages of solving more complex, demanding tasks of the data sets, and deep learning which requires traversing multiple data sets and highly-scalable algorithms that might be relatively at infancy today but will become prominent in the years ahead.

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