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Machine learning and the Internet of Things Posted on Mar 13 - 2018

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Gartner predicts that more than 65 percent of enterprises will adopt IoT products by the year 2020.

The growth of the Internet of Things (IoT) market in recent years is hard to ignore. According to Forbes, the global IoT market will grow from $157 billion to $457 billion between the year 2016 and 2020. The major contributors to the investment include leading industries like manufacturing, logistics, and transportation.

When it comes to sectors that dominate this investment, smart city initiatives and industrial IoT top the chart by owning more than 50 percent of the market. Gartner predicts that more than 65 percent of enterprises will adopt IoT products by the year 2020.

The heart of this process and what drives the real business value is encapsulated in the third stage of this activity chain, which is 'Transformation and Analytics'. This is the stage where the data is inspected and decisions are made. These decisions will directly influence the actions that will optimise business flows.

This is where the role of machine learning and artificial intelligence becomes significant. The ability of the system to make cognitive decisions based on historical data will greatly influence the value of the solution. Technologies like Azure Machine learning can leverage supervised learning techniques to help make business decisions based on classification, regression, and anomaly detection.

Machine learning - evolution

The concept of machine learning is not new to the world of computing. The birth of the term happened in the late 1950s, inspired from related fields in computing such as pattern recognition and artificial intelligence. However, leveraging this concept to optimise business process was largely constrained by the cost of provisioning and maintaining the compute and storage required to host and execute machine learning algorithms.

The primary cause for the re-emergence of machine learning is the evolution of cloud computing and its adoption in today's enterprise world. By offering features like infinitely scalable compute and storage, high-performance computing services, and pay-per-use subscription model, cloud computing became the ideal surrogate to bring machine learning back to life. This enabled organisations of any scale to affordably run machine learning algorithms to optimise their business processes. It also encouraged cloud market giants like Microsoft, Amazon, and Google to offer this technology as a software service consumable on a subscription model. View More

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