Six Key Data Center Design Considerations To Support Artificial Intelligence And Drive Innovation

Six Key Data Center Design Considerations To Support Artificial Intelligence And Drive Innovation

Businesses have been storing and accumulating data for decades without hardly realizing how entrenched in data the world has become. IT professionals continue to sift and parse data to improve business processes. Still, as the data loads become insurmountable, making sense of the data is just too much to tackle. Yet, what if intelligent algorithms could be written to accomplish this enormous task and derive the insights businesses need from the complex data stores they already have? Artificial intelligence has emerged to do just this – creating a beneficial relationship between the volumes of data and the ability to predict trends and guide more intelligent business decisions. Businesses can now capitalize on Ai intelligence with numerous applications for just about any problem their companies may ace. Naturally, AI-enabled hardware and software investments are expected to surge as companies better understand how to reap the value and how to transform their data center environments to support the demands of Artificial Intelligence.

How Does Ai Work?

Believe it or not, Artificial Intelligence isn’t a new technology. Coded computer instructions have been carrying out specific tasks and automating processes for human beings for decades. These same algorithms have grown more detailed and intricate such that Ai algorithms, a subset of machine learning, can now tell the computer to learn to operate on its own, gaining knowledge and running tasks with increasing efficiency.

The first Ai program was presented at Dartmouth in 1956. And while the possibilities seemed like science fiction at the time, the challenges that before slowed the pace of progress, such as computer storage limitations and high-speed connectivity, have advanced to unleash the capabilities of Ai.

Artificial Intelligence Possibilities Bring Imagination Into Reality

Artificial intelligence (AI) technology not only makes humans more efficient, but it is changing the shape of business. Now that we live in an age of machine learning, 5G, and big data, the capacity to collect data and leverage Ai for business applications is multiplying.

Use cases for AI include the internet of things (IoT), medical diagnosis, robotic assistance in manufacturing, contactless shopping, job candidate selection, and so much more. Smartphones in nearly everyone’s pockets and devices in homes already use Ai to execute daily routines. Machine learning is improving language translations. Self-driving vehicles are being tested in a handful of large cities. Forbes even featured an article on 13 mind-blowing things that artificial intelligence can already do.

What happens next in Ai feels closer to our grasp every day. Imagine a cardiac monitoring device that detects heart arrhythmia and automatically makes a doctor’s appointment for the patient, or recognizes cardiac arrest and immediately dispatches medical help. If AI has the potential to accelerate global efforts to protect the environment and conserve resources by detecting energy emission reductions, CO2 removal, and predicting extreme weather conditions, imagine how it will shape sustainability, climate change, and environmental problems. AI applications contain vast potential, from enhancing industrial design to exploring innovative ways to curb terrorism, predicting diseases here on earth, to tracking asteroids and other cosmic bodies in space.

Artificial Intelligence Technology Reshapes Digital Transformation In Commerce

This powerful technology is a central agent in the advancement of the digital transformation of society and the new digital economy. There’s virtually no major industry modern AI — performing objective functions using data-trained models — hasn’t already affected. Complex decisions can be made much faster and more accurately than ever before. Given that AI technology allows CIOs to generate new customer insights, save money, and predict customer behavior, Ai is not only on the rise but is quickly becoming indispensable.

So what can businesses expect from investments in artificial intelligence technology?

  • Reduced operational time: Ai can automate many operational tasks, freeing business leaders to tackle more complicated business problems and decision-making.
  • Virtual assistance: Chatbots and virtual assistance can significantly affect how customers interact with technology, answering simple questions and providing interactive customer service.
  • Personalized customer experiences: Ai can analyze vast data more efficiently, identify patterns in information such as buying history and preferences, then decipher common threads and provide customized service from millions of analyzed transactions.
  • Less human error: It’s true that human fallibility is a reality. While humans need to supply the context and understand nuanced situations, data science benefits from reduced error, delivering more accurate predictions and data analytics.
  • Greater business insight: Forecasting and prediction is a standard business practice that benefits from AI capabilities. Companies have long been trying to predict market shifts and consumer interests to prepare for what is coming. AI can process billions of data points in seconds and even use historical data to predict future outcomes with a high level of accuracy, allowing businesses to leverage greater intelligence and informed decision-making.

How Can Organizations Transition to Data Center Infrastructure Capable of Supporting AI?

To leverage Artificial Intelligence and its competitive edge, businesses need to consider the data collection, storage, computing, and connectivity architectures that support a data-driven business. Where Artificial Intelligence is the heart of digital transformation – data centers are the heartbeat of any IT strategy. A robust data center platform is required to support AI’s coverage, capacity, and connectivity requirements. To capitalize on that potential, businesses must create an environment where AI use is trusted, safe, and supported. Here are some leading considerations to achieve this goal.

1. High-Quality Data

If data-intensive projects have a single point of failure, it begins with data quality. For AI to reveal optimal insights, businesses must collect vast amounts of high-quality data. In fact, several of the latest practical applications for Artificial Intelligence require an increased data load to deliver the best quality insights. Businesses must also balance increased data demand with the cost of transmitting and storing that data.

Data also flows from a variety of sources. Data used as input for AI model training can be generated from cloud environments, IoT devices, edge systems, or data centers. Increasingly, the data being generated at the edge is quite large.

Colocation provides scalable space for data storage in a right fit model that gives customers full access to their IT stack without assuming the risks associated with operational management. Colocation also serves as the connectivity hub unifying public cloud, private cloud, and on-premise infrastructure through a connected Hybrid IT model. Colocation delivers a suite of carrier solutions and direct cloud connection products to connect businesses’ distributed data resources while stabilizing costs from escalating cloud egress fees and removing vendor lock-in.

2. Computational Power & Enhanced Cooling

It turns out that training AI models require a lot of power for the machine to learn and then perform tasks with speed and accuracy. According to Medium, compute power has increased 300,000 fold. This means that for enterprises to truly take advantage of the capabilities offered by Ai, data center efficiency and power performance takes on even greater importance. Think sophisticated algorithms, predictive modeling, and more; they must have the computational power to process and manage big data sets in scalable and flexible architectures.

Energy-demanding artificial intelligence applications are known to use significantly more power per rack. Though the average rack density is 8.4 kilowatt (kW), AI applications commonly use more than 30 kW per rack. Power consumption demands like this exceed standard data center or on-prem power design standards and require alternative cooling methods beyond fan cooling. Without sufficient cooling, facilities risk servers overheating, which can result in equipment failures and downtime.

Colocation data centers support the most intense power demands, including hyper-scale deployments. Experienced providers constantly evaluate capacity capabilities and employ innovative ways to deliver specialized power and cooling configurations to support high-density workloads. This includes building or adapting facilities to meet sustainability requirements, implementing innovative cooling solutions, and leveraging customizable liquid cooling options to provide an optimal environment for high-density workloads.

Colocation providers can also deliver redundant power strategies to minimize downtime. From one-rack to multi-megawatt solutions, the range of power and density options allows businesses to meet demand while right-sizing their solution to just what they require.

3. Diverse Locations and Edge Proximity

Edge computing sets up a new computing paradigm that moves Ai and machine learning to where the data generation and computation actually occur – at the network’s edge. Enterprises require robust networks and infrastructure in colocation data centers to effectively manage and protect the ever-increasing volume of data and push the hosting of AI stacks to the edge, closer to data sources.

Unlike an on-premise data center built to serve the business headquarters, colocation data centers with a nationwide portfolio are available to help organizations place data and compute in facilities to support distributed architectures near end users at the edge. This allows for the extremely low latency performance required to enable today’s AI and emerging 5G applications.

4. Network Connectivity

AI workloads demand a reliable and highly scalable network. AI applications do not exist in isolation, requiring integration with various other IT systems instead. In many situations, an enterprise needs to import data from external sources to improve the accuracy of its AI models because it does not have enough data of a particular type or it needs to fuse different variety or types of datasets (for example, weather, traffic and so on). These external datasets can reside in multiple cloud environments and private data centers or be generated at the edge via IoT devices.

A colocation data center can serve as an interconnection hub and provide secure, high-speed, low-latency connectivity to multiple data source environments. A data center’s portfolio of network “peers” further allows organizations to align themselves with various carriers to ensure availability if one carrier fails.

Colocation data centers offer private bandwidth for greater performance. Businesses no longer have to compete for bandwidth over the public internet when they have their own dedicated pipe. The ability to scale bandwidth for low latency processing speeds and data delivery as needed also ensures information is not delayed. This is particularly essential with real-time and near-time AI applications such as self-driving cars and life-sustaining health devices.

5. Direct Cloud Connection

Given the amount of data used in AI learning models, businesses need to access the cloud for storage and processing. Colocation data centers with direct cloud access allow enterprises to quickly expand their storage capacity and access cloud services as needed. Providers with direct onramps to major cloud providers can also deliver this added capacity at a lower cost by minimizing egress charges.

6. Uptime & User Experience

Reliability has always been critical for any organization; this is true for artificial intelligence applications. To power business operations, analyses, and decision-making, the availability of the IT environment is critical. Uptime is at the core of user experience when running seamless Ai applications; conversely, downtime means production stops. While disruption in an AI-enabled application, such as a delay in regulating home heating or a lag in a navigational upload, would be an inconvenience, an issue with a growing number of AI applications – particularly those that deal with health and safety – could have more detrimental effects.

If your colocation partner isn’t guaranteeing at least five nines (99.999%) of uptime —less than six minutes per year of downtime—then you might not have a highly reliable partner. And according to Uptime Institutes 2022 annual Outage Analysis report, we now have data suggesting public cloud outages are getting worse, and the impact of high outage incidences results in even more significant financial losses. Gartner calculated in 2014 that businesses could lose well over $300K on average in just an hour of downtime—a figure that has only increased in the last several years.

Third-party colocation data centers employ a series of redundant systems, power feeds, and connectivity options to ensure operations remain online if one system or network path fails. Trusted providers also back up this reliability with 100% uptime SLAs. In addition to these redundancies, third-party data centers purposely choose locations outside of flood zones and other areas affected by extreme weather to mitigate further risks associated with natural disasters that on-prem data centers housed in business headquarters cannot often accomplish.

Preparing Your Enterprise for an AI-Enabled Future: The Data Center’s Role in Supporting AI

The massive quantity of data it takes to develop human-like intelligence cannot be underscored enough. Coupled with continuous learning cycles for near-constant data ingestion is necessary to foster ongoing learning and deliver more accurate decisions, predictions, and solutions. Data storage and transmission requirements for businesses will only grow.

Not every data center can support Ai workloads. Enterprise data centers generally lack the necessary infrastructure and flexibility. However, hyper-connected colocation data centers have the requisite network communications equipment, cloud access, scalability, intense power, and high-performance availability to support the AI workloads that are taking on more prominent and essential roles in our business and daily life.

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