FEATURE: MACHINE LEARNING
FEATURE:
of more consistent decision making and
streamlined operations.
Garnering enterprise momentum
With Machine Learning seeing an increase in
enterprise wide adoption in the MEA, pundits
say CIOs and C-level executives need to be
flexible when deploying new technology.
Adam Pantanowitz, Co-Founder and Chief
of Innovation at AURA, said there is no
doubt that there is greater accessibility
to and democratisation of Machine
Learning, an upsurge and excitement
around the technology.
Pantanowitz added that the big cloud
players have embraced it and made their
tooling more accessible.
“The open source community are very
active and are providing accessible toolkits.
In addition, the scientific community have
made some great advancements which
allows one to leverage knowledge gained
in another domain by another researcher
and their resources and move it to your
problem domain.”
He said that computation has certainly
advanced, enabling more cost-effective access
to the requisite processing power (GPUs and
CPUs) to enable these applications.
Pat Gelsinger, CEO (VMware), said Machine
Learning is the most important new
workload to emerge in the IT enterprise
space in the last 10 years. Today, said
Gelsinger, Machine Learning is seen as being
the way of the future by many enterprises
as it is deemed an effective and automated
means to provide companies with a
competitive advantage, driving innovation
and ultimately increase profitability.
“The main sectors utilising Machine Learning
are healthcare, travel and hospitality,
retail, finance and manufacturing. By using
Machine Learning, a business can prevent
fraud, provide dynamic pricing, effect
predictive maintenance, leverage alerts
and diagnostics as well as use real-time
information to drive business decision
making. This may be achieved by intelligent
operations management, from applications
to infrastructure, all by using dynamic
threshold and capacity planning,” he said.
Secret sauce
With vendors often claiming to have some
Machine Learning ‘secret sauce’ in their
wares that will revolutionise an enterprise’s
business how should CIOs and their IT
teams go about selecting the right tools
and systems?
Dr Nicola J. Millard, Principal Innovation
Partner, BT, reiterated that no amount of
secret sauce will revolutionise a business
unless its data is in order and of high quality.
According to Millard, Machine Learning does
not work by magic as it depends on data
and if that data is unstable, inconsistent
and spread among multiple legacy
systems, it becomes more difficult and far
more expensive to do. “Aside from data,
enterprises also need to step back and ask
what problem they want Machine Learning
to solve and whether it will actually solve
it. For example, if they want to deploy a
chatbot to improve customer experience,
does it actually improve it or just add
another level of frustration for customers
if the ‘bot’ hits a dead end and abandons
them if it can’t understand what they want,”
she said.
Enterprises need to understand how
Machine Learning will integrate with legacy
systems, processes like in the contactcentre,
how much training it will require and what
the business case is given that return on
investment (ROI) may take some time.
Tony Bartlett, Director, Data Centre
Compute at Dell Technologies South
Africa, agreed with Millard adding that:
“Firstly as with most systems, enterprises
are advised to look to vendors who can
provide an end-to-end solution. Machine
Learning systems are part and parcel of a
broader business strategy, which includes
Digital Transformation, cybersecurity, Edge
Computing, automation and data analysis
to name a few.”
Bartlett said enterprises should consider
whether the vendor they rely on for Machine
Learning solutions has the technology depth,
breadth and specialisation to meet their
requirements whether it be at the Edge,
core or cloud.
“As the volumes of data increases and
systems are tasked with processing more
data, in real time, IT systems will need to
depend on high performance computing to
keep up with the demands and technologies
such as GPU’s, FPGA’s, high speed memory
and storage are essential in deriving
outcomes in real-time,” he said.
Data quality
With data and AI playing a crucial role in any
Machine Learning deployment, experts are
urging CIOs and business line executives to
ensure data quality.
Ramprakash Ramamoorthy, Product
Manager, ManageEngine Labs, said 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.
“Organisations should track datasets
and make sure they comply with local
regulations and are free from inherent
biases. Sometimes labelled data availability
would be a challenge. For example, there
are practically no commercial grade labelled
datasets for service desk sentiment analysis,
but you have a wide range of datasets
available for e-commerce product reviews,
hotel reviews and movie reviews,” he said.
Ramamoorthy pointed out that transfer
learning techniques can help bootstrap your
small service desk dataset with the learning
from the larger consumer datasets and
whatever process used to generate and label
datasets, will have to run fairness checks
before deployment.
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