INTELLIGENT BRANDS // Data Centres
The rise of AI and the next
generation data centre
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Ian Jansen van Rensburg,
VMware EMEA Senior
Systems Engineer, looks
at the rise of Artificial
Intelligence and the birth
of the next generation
data centre.
1. Compute (the need for speed): From
CPUs and GPUs through FPGAs and
ASICs, computing resources have made
incredible progress in the past few
years, allowing us to process data more
quickly, more broadly, and more deeply
than ever before. In addition, new
deployment channels (such as public
cloud GPUs/ASICs) allow customers to
balance Capex versus Opex in their
AI initiatives.
2. Algorithms (the modern-day equation):
Algorithms are the theoretical foundation
underlying Machine Learning and AI,
from simple neural networks to more
complicated recurrent and convolutional
architectures. Many of these algorithms
trace back decades – yet have only
recently led to applied breakthroughs.
Ian Jansen van Rensburg, VMware EMEA
Senior Systems Engineer
A
rtificial Intelligence has rapidly
become a leading driver of
innovation, creating competitive
advantages and new business opportunities.
The proliferation of data is enabling
breakthroughs across disparate industries,
from transportation and healthcare to
energy and communications. However,
one of the most profound AI-mediated
transformations will occur within the world of
enterprise technology.
Based on our extensive experience within
the enterprise tech stack, we see three core
factors that have created the perfect storm
fuelling today’s AI innovation:
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3. Data (data is the new oil): Machine
Learning techniques are famously
data-inefficient, compared to humans.
Without sufficient training volume,
Machine Learning techniques fail to
reach acceptable performance levels.
And as recently as a decade ago, the
quantity of enterprise data available
for Machine Learning was a tiny
fraction of what is available today,
from logs and metrics to traces and
configuration events.
The AI opportunity: Self-optimising
data centres
This explosion of operational data is both
a blessing and a curse. In the current
world of data centre and cloud operations,
companies are desperately trying to keep
up with the flood of raw information and
falling further behind each year. The volume
of data has outpaced currently available
tools and platforms, placing an increasing
burden on human operators – even feature
developers – to keep up.
AI will fill the void between operational
complexity and operational capability. Some
common uses where companies can leverage
AI to improve their data centres include
improved operational efficiencies, real-time
cost-performance balancing, security, and
even business metric optimisation.
Don’t fight the data deluge –
embrace it
The increase in operational complexity won’t
be slowing down anytime soon. The gap
between human scale and machine scale
continues to grow. Organisations that are not
able to augment their data centre, cloud and
edge computing strategies by adopting AI
technologies will risk falling further behind.
We envision a hybrid data centre, cloud and
edge that is self-healing and self-optimising,
greatly reducing administrative overload
and allowing firms to focus on strategic
innovation and customer experience.
What next?
Preparing your data centre for the modern
world is not a trivial task. Start by future-
proofing your data centre as groundwork
for the AI-driven approaches that are
coming around the corner. As for the data,
consider implementing edge computing,
which enables data gathering and analytics
to occur near the source of the data.
Companies should also invest in software-
defined infrastructure as a key enabler of this
process. Last, but certainly not least, explore
a multi-cloud strategy to offer the most
agility and flexibility to your IT infrastructure
as you prepare for the high-velocity,
machine-learning-driven future. n
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