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.