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IoT and Machine Learning

A Thrivingly Connected Future

An idea – a machine that is intelligent enough to make decisions without human interference. This idea has captured humans for decades. In the 21st century; with the apex in IoT technology, we are witnessing what ancestor’s considered “harebrained idea”. The future is IoT – Machine learning is a way towards it. The potential of IoT is infinite – with the integration of Machine Learning and Artificial Intelligence; the competence to deploy, automate, orchestrate, and secure data; smartly.

Enabling billions of devices connected over a single network simultaneously is not the target; utilizing the heaps of data gathered from IoT devices which assists the automation of business processes is. The growth of Machine Learning is directly proportional to that of IoT’s growth.

If we take into consideration the Industrial IoT, the firms are continuously feeding the immense amount of data into the Machine learning models to provide customers/consumers improved user experience, operate progressively with the help of results, etc. In recent years, Machine Learning has become a “workhorse” for almost every industry, especially, Information technology. From predictive maintenance to voice recognition; the uprising technology is embedded into everything.

Another important aspect of Machine Learning is the elimination of human error. For IoT devices to reach its maximum potential; Machine Learning is key. In the field of Software Technology; the disruption caused by Machine Learning (AI) is no exception – the traditional cycle of software development is enhanced and quick compared to langsyne. With the hype surrounding Machine learning and IoT; the actual value of both individuals as well as in confluence to each other amisses majority of people. Businesses often look at these two terms and think “Do we need this?” “Is it worth the time, effort, and money, or simply a buzz?”

The usefulness of Machine Learning

Machine Learning is a powerful term capable of revamping an entire organization’s structure in a matter of weeks. Churning a plethora of data in a day, an organization can redesign their operations, services provided and utilize insights for the betterment. But, a question arises: IoT Analytics does the same thing; why use/integrate Machine Learning’s complications into the business model?

The answer is quite simple and straightforward: IoT Analytics is naturally static for analysis, utilized for unstructured data, and providing data. When IoT is discussed, it is important to have to determine the correlations between scads of sensors’ statistics and externalities which rapidly amasses enormous data with millions of quantitative points.
So, when is Machine Learning a valuable resource for an organization? Only when an organization knows what they want from the accumulated data, but are unfamiliar with significant input variables to make the “right decision.”

When Machine Learning is involved; the only work a human has to do is feed the algorithm data. That’s it. The rest of “learning”, “improving”, and “producing better results every day” is its job description. Machine learning is a dynamic analysis algorithm which operates on IoT-collected data from various sources and learns to improve and improvise, without human interference.

Machine Learning’s Evolution and IoT Convergence

Machine learning has been a concept and emerged in the last 1950s, but due to the low processing potential, storage issues, and heavy cost; the idea was dropped until the birth of Cloud Computing and moving forward to IoT. The adaption of machine learning in today’s corporate world since it offers numerous benefits for organizations, irrespective of their nature, is what caused its re-emergency.

Although machine learning improves itself by acquiring the knowledge from the data it collects; to make cognitive decision-making or “human-like” for the use of the better word, it requires holistic data sets.

The convergence of machine learning and IoT for business optimization works hand-in-hand. There are multiple reasons why organizations are remodeling their data processing structures, rapidly. No business would want to miss the crucial point which can assist them in boosting sales, generating better revenue, providing customers with improved recommendations according to their preferences, gain better insights into business, etc.

Instances when Machine Learning and IoT exhibits perfect collaboration of “working together, intelligently.” A few of the scenarios are as follows:

Vehicle quantization

Machine Learning Evolution to IoT Convergence

As the name suggests, IoT and Machine learning plays a huge role in vehicle quantization. With the help of machine learning’s data analysis and IoT’s sensory data inputs consisting of millions of data points from a vehicle’s surrounding environment; the confluence of there two buzz terms, analyzing events daily from the vehicle’s point of view, improving: security, robustness, driving instances and experiences.

Peculiarity Inspection

Peculiar or anomaly detection can be done with the help of machine learning algorithms in real-time data. Data feeds being sent by IoT devices produce similar anomalies, monitored through machine learning features. The dips and peaks; either positive or negative, are readable by a machine learning algorithm that is being streamed by IoT sensors/devices. With the help of these data points, machine learning presents with helpful insights and appropriate decision options.

Proactive Maintenance

One of the primary concerns of any organization is to have cost-efficient proceeding. They do not wish to compromise on quality nor customer’s needs but wish to have a viable manner to run their company. Proactive maintenance is a feature of machine learning; the prime reason for its resurgence and hype in the corporate world. Integrated with predictive capability, machine learning forewarn person liable about prospective issues, avoidable delays, the residual service life of a machine(s), reasons for non-compliance, and failure within an organization. With this, businesses ensure to provide optimum operational expenditures by risk mitigation, minimizing the maintanence time; significantly yet intelligently.

Machine learning and IoT are having intersecting points in the technological realm – one being the data transmitter, the other: the brain. With the heaps of data comprising of image/voice recognition, forecasting, process optimization, and predictions; machine learning and IoT’s convergence has benefitted the corporate world in ways, unimaginable.