Sunday, 27 October 2019 23:03

Exploring the role of machine learning in business success

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Data science is a complex, dynamic field of work with many industries to specialise in, from statistics to machine learning. In today’s article, we will be exploring one of these popular specialisations in data science: machine learning.

What is machine learning?

The term ‘machine learning’ was first coined in 1959, but it is only now that technology permits us to take advantage of the field of data science as may well have been first envisaged all those decades ago.

Machine learning (ML) is used to make predictions or decisions without being explicitly programmed to perform the task. This is done using algorithms that create statistical and mathematical models of the data being worked with, and while it is seen as a subset of artificial intelligence, it needs highly skilled human intelligence to set the parameters, make sense of the results and deliver actionable intelligence.

This is just one of the examples of the sophisticated thinking needed to find the simple answers that lie underneath layers of increasingly complex, interwoven webs of data, and businesses are turning to people with the advanced technical and mathematical skills (data scientists) to unpick complexities and make sense of the numbers.

What does machine learning look like in practice?

Machine learning improves processes across many different industries.

In the healthcare industry, machine learning is used to help wearable technologies and sensors update patient health in real time.

There are countless tech companies that are investing in, and utilising machine learning and artificial intelligence. Some of these companies include Amazon, personalising product recommendations, or Netflix, which delivers personalised recommendations based on algorithms.

According to a recent report, there is untapped potential for Government agencies to use machine learning, with another report exploring five ways chief digital officers can help encourage machine learning in the public sector, exploring the below uses of machine learning for the public sector in more depth here:

  1. Automating data science
  2. Reducing the need for training data
  3. Evolving technology
  4. Transparency of results
  5. Local deployment

The retail industry can use machine learning to improve the shopping experience, helping fight back against online disruptors while providing more personalised service, including real-time pricing adjustments, coupons and other incentives.

Likewise, automotive industries see all manner of benefits, be it in improvements to manufacturing processes, operations or maintenance, through to the customer service process - before, during and after sales are made. Transportation industries are also able to analyse patterns and trends to make routes more efficient, to optimise delivery experiences and to proactively predict problems and solve them before they occur.

The future of machine learning and the role of a data scientist
Data scientists are key players in harnessing these insights to inform business decisions. Whether it is in forecasting, product recommendation, personalisation, intelligent chatbots, better customer service and more, machine learning's applications to life and business are immense.

How to start your data science career
There are countless ways to start your career in data science. If you are interested in a data science career and completing a postgraduate degree in this field, the University of New South Wales’ flexible online data science program is the ideal way to gather the skills you need.

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Now’s the Time for 400G Migration

The optical fibre community is anxiously awaiting the benefits that 400G capacity per wavelength will bring to existing and future fibre optic networks.

Nearly every business wants to leverage the latest in digital offerings to remain competitive in their respective markets and to provide support for fast and ever-increasing demands for data capacity. 400G is the answer.

Initial challenges are associated with supporting such project and upgrades to fulfil the promise of higher-capacity transport.

The foundation of optical networking infrastructure includes coherent optical transceivers and digital signal processing (DSP), mux/demux, ROADM, and optical amplifiers, all of which must be able to support 400G capacity.

With today’s proprietary power-hungry and high cost transceivers and DSP, how is migration to 400G networks going to be a viable option?

PacketLight's next-generation standardised solutions may be the answer. Click below to read the full article.


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