As , the world’s IoT market will touch $457 billion in value by 2020. Manufacturing, transportation and logistics currently take up especially large shares of the market, but yet more firms could soon share in proceeds of the IoT revolution.
Research company Gartner believes that, come 2020, over 65% of enterprises will have taken up IoT products. Businesses have the option of sourcing a tremendous amount of data through IoT products, but how can they effectively utilise this data to further their own growth? Machine learning could prove an especially effective solution for making this growth possible.
What is machine learning?
Though a sophisticated stream of artificial intelligence, machine learning is far from a new phenomenon in computing. The term “machine learning” was actually coined in the late 1950s; however, optimising business processes through the use of machine learning has long been beyond the practical means of many companies.
This can be attributed to the complexity of machine learning algorithms compared to algorithms used in more conventional strands of computing. Usually, a computer will solve a problem because it has been programmed specifically to do so. However, ML algorithms differ in using large amounts of data to guide their decisions and predictions.
Email software, for example, can sort spam emails from more meaningful correspondence by picking up on such phrases as “Nigerian Prince” which commonly feature in spam emails. Meanwhile, Netflix can recommend you new movies and shows by considering content you have previously bought. These are straightforward examples of machine learning in action, of which lists more .
Why are more firms now acting on machine learning?
Traditionally, businesses interested in making use of machine learning have been hamstrung by the expense of provisioning and maintaining the necessary computer and storage for hosting and executing ML algorithms. However, initiating these algorithms has become a more palatable option due to advances in the nature and adoption of cloud computing.
Today, many businesses can readily draw upon cloud computing solutions enabling them to infinitely scale compute and storage to meet their machine learning needs.
These businesses also benefit from high-performing computing services which can stay within their monetary reach due to the availability of a pay-per-use subscription model.
As aptly describes the situation, “cloud computing became the ideal surrogate to bring machine learning back to life.”
How IoT has made machine learning more effective
Despite the much-vaunted merits of machine learning, the effectiveness of its algorithms can still heavily rely on what data is fed into them.
An abundance of relevant data can impressively fuel an ML algorithm much like useful clues can help a detective to reach wiser conclusions.
It is exactly for this reason that IoT can make an ideal use case for the technology. A wide range of IoT devices can highly frequently generate data which can then be placed into an ML algorithm. For example, information provided from devices on which a firm strongly relies can help that firm – or, more to the point, machine learning – foresee how those devices could falter or how long they might remain functional. These revelations can then assist the business in trimming maintenance time.
Transportation and logistics industries can also be attracted to machine learning. This is because ML can source extensive data from vehicles to assist in enhancing the safety and reliability of such.
More businesses set to put their heads in the cloud
Of course, for businesses to maximise the effectiveness of machine learning, they will need sufficient access to the cloud. Fortunately, the public cloud looks set to extensively grow in the enterprise space. This is one major revelation of a cloud market survey carried out at the AWS Re:Invent conference in November, where “some 200 enterprise IT executives” comprised the respondents.
That’s according to that a further 88 online enterprise IT respondents were also surveyed. Roughly 37% of workloads run by the survey’s respondents were on-premises; however, this looks likely to fall to approximately 27% by 2020.
Meanwhile, the public cloud hosted roughly 31% of their workloads; however, the figure should climb to approximately 41% by 2020.
Private and hybrid clouds are also expected to grow in usage in that time. Businesses can also be inspired by these worthwhile IoT examples highlighted by RedPixie, a respected provider of cloud solutions.
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