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Connect Your IoT Deployments to Machine Learning Featured

Connect Your IoT Deployments to Machine Learning photo of outer space

As the number of internet-connected sensors continues growing due to increased applications in cars, planes, buildings, and home devices, businesses generate vast amounts of data. According to a study, there will be more than 50 billion IoT devices in the world by 2025. This is up from 9 billion in 2017. With this significant rise in data, tapping it to extract useful information is becoming a big challenge that traditional methods cannot meet. This is where machine learning (ML), one of the subsets of artificial intelligence (AI), comes into place.

With the wave of technology on the rise and the need to gather insights, customers are increasingly demanding devices that can give them insights and at the same time keep their data safe. IoT's AI-powered capabilities ensure that this is achieved by transforming data, enabling analysis, visualization, and embedding across different devices, gateways, data centers, and ecosystems. IoT is a subset of edge intelligence. An intelligent IoT system comprises mechanical and electrical components, sensors, processors, storage, and software. It also has ports, antennas, protocols, and in-built analytics to train and run AI models.

For IoT to be successful, tens of billions of devices sitting at the edge in homes, offices, factories and oil fields, and automobiles need to be connected. Machine learning will play a critical role in the deployment of IoT applications. It will unlock the potential of IoT through its ability to wring insights from data. Machine learning, a subset of AI, enables IoT devices to identify patterns and detect anomalies automatically in intelligent sensors and smart devices. Connecting IoT with machine learning will lead to the development of appliances such as refrigerators and others that will automatically detect anomalies such as temperature, pressure, sound, and humidity.  

Unlike the traditional business intelligence tools, machine learning approaches are highly efficient and can make predictions that are up to 20 times faster and incredibly accurate. Machine learning also can enable organizations to avoid unplanned downtime, enhance operational efficiency, increase innovation and promote management of risks. Predictive capabilities of machine learning are beneficial in manufacturing setup.

By gathering information from different sensors in machines, machine learning algorithms can do calculations and understand the abnormalities and unusual starts in machines. Seeing when a machine needs maintenance work in advance is critical. It converts time and resources that would have been wasted into some dollars in spared costs. Some companies now use machine learning to predict with up to 90 percent accuracy when machines will require maintenance work, meaning massive cost cuttings. In various sectors such as oil and gas and industrial manufacturing, unplanned downtimes due to equipment breakdown can be costly. With analytics, predictive maintenance can mitigate losses of unplanned downtimes.

Just as machine learning and AI can help predict machine failure, they can also improve operational efficiency. It can predict operating conditions and identify parameters that need to be adjusted to maintain the correct results. ML can analyze constant data streams to detect anomalies that are invisible to the human eye or simple gauges. Machine learning helps find erratic insights, therefore, increasing profitability through operational efficiency.

Connecting IoT with AI can also lead to the direct creation of new products and services. In logistics and transport industries, fleet management has benefited immensely through AI, which monitors data points in trains, trucks, and even planes to find better routes and perform scheduling through data. This also reduces time wastage and downtimes.

Once IoT is connected to machine learning, companies are given an edge through trend analysis which will improve performance.

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Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

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