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As the demands of AI grow, infrastructure requirements take new shape

Written by Eltjo Hofstee, managing director, Leaseweb UK

Recent research by Accenture has shown that 85% of businesses expect to make significant investments in AI-related technologies over the next three years. AI is changing everything, and as it becomes more prevalent, organizations will be forced to come to grips with it on a macrolevel as it changes entire industries, and on a microlevel as it impacts business strategy within their ranks. With such consequential change happening at such a brisk pace, some key aspects of AI are worth keeping an eye on as it becomes more pervasive and organizations face a new world of processes and requirements.

From an infrastructure perspective one thing is clear, as AI moves beyond experimentation toward adoption, it will demand significant computing resources and infrastructure costs. Overheads will snowball as the technology becomes more complex and resource-demanding, and in a world increasingly impacted by AI, finding cost-effective environments to run the intensive processes will be both a requirement and a competitive advantage.

Businesses will have to adapt and be flexible, especially with regard to infrastructure. Cloud technologies, particularly hybrid cloud solutions, are and will be the foundation of AI as its needs for substantial amounts of data ratchet up. Hybrid cloud solutions will ensure that the needs of businesses and workloads match technology to the demands increasingly required to sustain AI.

Therefore, the biggest question for organizations is: what infrastructure allows for the continual use, development and implementation of AI without sacrificing performance? There are some things to keep in mind when evaluating potential partners to ensure choosing the best platform possible.

High computing capacity

To fully take advantage of the opportunities presented by AI, organizations need sufficient performance computing resources including Central Processing units (CPUs) and Graphical Processing Units (GPUs). A CPU-based environment can handle basic AI workloads, but deep learning involves multiple large data sets and deploying scalable neural network algorithms. For that, CPU-based computing might not be sufficient. For example, GPUs can accelerate deep learning by 100 times compared to traditional CPUs. Computing capacity and density will also grow, as will demand for high performance networks and storage.

Storage capacity

It's fundamental that your infrastructure has the ability to scale storage as the volume of data grows. Figuring out what kind of storage an organization needs depends on many factors, including the level of AI an organization plans to use and whether they need to make real-time decisions. For example a FinTech company that uses AI systems for real-time trading decisions may need fast all-flash storage technology, while for other companies slower but very large storage will be the most suitable solution. Businesses need to factor in how much AI data applications will generate. AI applications make better decisions when they're exposed to more data. As databases grow over time, companies need to monitor capacity and plan for expansion as needed.

Networking infrastructure

Networking is another key component of an AI infrastructure. Deep learning algorithms are highly dependent on communications, and networks will need to keep stride with demand as AI efforts expand. That's why scalability must be a high priority, and that will require high-bandwidth, low-latency network. The best choice for expansive service is a global infrastructure provider who can ensure the service wrap and technology stack are consistent in all regions.

Security

AI can involve handling sensitive data such as patient records, financial information and personal data. Having this data breached will be a disaster for any organization. Also, the infusion of bad data could cause the AI system to make incorrect inferences, leading to flawed decisions. The AI infrastructure must be secured from end to end with state-of-the-art technology.

Cost-effective solution

As AI models become more complex, they become more expensive to run, so getting extra performance from your infrastructure is pivotal to corralling costs. Over the next few years, we can expect continued growth in companies using AI placing heavier burdens on the network, servers and storage infrastructures to enable the use of this technology.

By making careful choices and identifying providers who can offer cost-effective dedicated servers, there is opportunity to boost performance. This will enable companies to continue investing in AI without an increase in budget.

Trusting the choice you make

AI is making technology more intuitive and more natural for users than ever before. It is imperative to choose a partner who will grow and evolve its platforms with a customer-centric and individualized approach.

The bottom line is that if organizations are looking to fully harness the power of AI, they need to seek out environments that can handle their needs and understand what is relevant to their business plans. This can only be done by choosing a partner you can trust and that can adjust to your changing needs, because it isn't a short-term business decision, it's a long-term technology commitment.

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About the Author

 

Eltjo Hofstee is Managing Director of Leaseweb UK Ltd. Eltjo has also worked for a number of companies in the hosting and media industry, and as a CRM (strategy) consultant. Eltjo has a master’s degree in public administration from Leiden University and during his free time he enjoys outdoor sports like rowing, mountain biking, skiing, but most of the time only has time for running.

Published Monday, May 20, 2019 7:27 AM by David Marshall
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