Q + A + Q + A + Q + A + Q + A + Q + A + Q + A + Q + A +
MOHAMMAD
JAMAL TABBARA,
SENIOR SOLUTIONS
ARCHITECT,
INFOBLOX
EDITOR’S QUESTION
Given that Artificial Intelligence (AI) is witnessing wide
enterprise adoption across various industry verticals in
the Middle East, it comes as no surprise that Machine
Learning, which is a subset of Al is also seeing broader enterprise
deployments. The fact that Machine Learning systems can be
deployed almost everywhere and in every industry vertical is in itself
an added advantage. We are seeing Machine Learning being used
in the security sphere, network and systems processes that revolve
around intelligence and analysis. What this is all leading to is better
business decisions being made by organisations that have invested
in this technology.
That said, enterprises across the Middle East should always evaluate
the potential vendors before selection a brand or solution they see
fits their business objectives. This evaluation
should be based on technical and commercial
aspects and ensure that the best vendor
solution that helps the business should be the
one that is selected for deployment.
Because Machine Learning can help
enterprises in multiple aspects of the
business processes, whether directly or
indirectly, it is critical that any deployment
in an enterprise organisation should add
agility, precision and intelligence. Enterprises
with Machine Learning embedded in
their decision making processes have an
advantage over their competition who still
rely on traditional technologies.
Some of the main drivers that have aided
Machine Learning implementations in the
region are Digital Transformation and AI.
The MENA region has seen a steady rise
of Machine Learning implementations in
enterprises with almost all verticals touting
a deployment. I can’t think of a vertical that
cannot benefit from Machine Learning.
Because Machine Learning is a type of AI
that allows software applications to become
more accurate at predicting outcomes
“
IT IS CRITICAL
THAT CIOS AND
IT LEADERS IN
GENERAL, LOOK
AT MACHINE
LEARNING
DEPLOYMENTS
BASED ON THE
BUSINESS
DRIVERS AND
SETTING THE
ORGANISATION’S
PRIORITIES
ACCORDINGLY.
without being explicitly programmed to do so, it is important that
Machine Learning algorithms use historical data as input to predict
new output values. What this requires is data integrity because
there is always the potential for a false positive
or missing some accurate decisions during
Machine Learning training processes even
vendors compete to minimise the margin of
error as much as possible.
Therefore, it is critical that CIOs and IT
leaders in general, look at Machine
Learning deployments based on the business
drivers and setting the organisation’s
priorities accordingly.
False positives are common. For example,
the machine learning process might
identify a certain traffic type or a pattern as
malicious due to its analysis, yet this traffic
type or pattern might be legitimate despite
being uncommon.
For this reason, your model is as good as the
data you train on and spurious correlations,
biases, data imbalance present in your data
can adversely affect the quality of Machine
Learning model’s prediction.
While Machine Learning is not a very recent
trend in IT, it is not going away any time
soon. It is here to stay and it will only get
better as the ROI starts to manifest in the
form of positive impact on the business. •
www.intelligentcio.com
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