What is the scope of Machine Learning in IoT today? Are IoT entrepreneurs looking for Machine Learning engineers?

 The start of a new technological era is achieved through the fusion of machine learning with IoT. By automating the repetitive processes, Machine learning has brought a sea of changes. IoT has brought in significant changes in several processes by allowing machines to communicate with each other in the same way.

When the two self-developing technologies are brought together, images of the scope for growth and development. Data scientists believe that we are still in the nascent stage, and there is a whole new world of data science job opportunities that we will have to explore through IoT and ML are being used across horizontals and vectors.

Scope-of-Machine-Learning

What is IoT?

The network of machines that can be programmed to work in synchrony is the Internet of Technologies or IoT. These devices are programmed in terms of interacting with the internal and external environment to react accordingly. It can be anywhere from 2 or 3 to hundreds with the number of devices that can be connected in an IoT network.

The complete network is built based on sensor technology. In smart homes, precision farming, automated cars, connected grids, smart retail, and lots more where IoT finds applications with the help of data science engineers. The market size of IoT has been gradually growing from 2012 in sectors including healthcare, consumer electronics, transportation, and others, as shown in the Market Research of the Grand View Research.

What is Machine Learning?

The part of artificial intelligence dealing with machines that are able to self-learn and do not require any expressive coding is Machine learning. It uses automation of the work processes, engages the visitors to a website, and works on the tasks that are repetitive.

The part of machine learning is the neural network. There are a lot of experiments that are being done to build neural networks that are replicating the functions of the human nervous system as it is a relatively new domain. We have a long way to go as we are still in the initial stages of the research and application in this field.

 

IoT and ML – A Symbiotic Relationship

Embedded technologies are connected using wireless and wired communications in an IoT network. Data is considered one of the most vital components for the development of an IoT network. The system should be able to have access to the raw data through varied sources processing it for gauging the external and internal parameters.

It is where the Data Science career path contributes to being the subset of Machine Learning that becomes vital. It needs to work efficiently as it needs the right information that can be collated using the techniques of data science and models, including the clustering methods, classification methods, and neural networks for an IoT network.

IoT with Machine Learning – What are the future possibilities?

We can now expect to see in the future by bridging ML and IoT as there are several possibilities. The following are the possibilities:

●      Self-driving cars

For quite some time now, the concept of self-driving and autonomous cars has been around. The first semi-autonomous car developed, as a matter of fact. It is since then that there has been significant research and developments that have been included in building these cars.

●      Robotic Vacuum Cleaners

These robotic vacuum cleaners can navigate their way around your home along with the vacuum for you as they are designed compactly. They can easily reach out to the crevices and hard corners to remove dust. These robotic cleaners were designed for operating with the use of a remote control mainly.

However, you can be a data scientist with ML algorithms that are used for self-operating. The benefit being the use of robotic cleaners is that it requires no supervision.

●      Smart HVAC

It consists of every appliance that is used for heating, cooling, and ventilating the space. This smart thermostat can automatically adjust the temperature of the air conditioner along with the heater that is based on external conditions. This system can send your connected device a notification when it is time to get the air filters replaced.

●      Risk Management

IoT and ML are the applications of both service management and product development. These two technologies are used in tandem for identifying the possible causes of the system or the hardware failure in advance and preventing them. It will aid in averting conditions that can create operational issues.

●      Smart Energy Management

In smart energy management, both IoT and ML can be applied together. These technologies can be used effectively for optimizing the consumption of energy that can be done through the construction of smart grids.

Final Thoughts

For analyzing the data and gaining insightful information, data analytic techniques and tools are used effectively. It can be used for building an integrated Internet of Things network as this information can be applied by the ML algorithms for building self-learning machines. There are several opportunities for development and research in the platform of machine learning and IoT, and it is where your future lies!

 

The IoT Academy is the one-stop platform for all your queries related to Machine Learning, Data Science, and IoT. With dedicated mentors, you can aspire for that dream job by acquiring the necessary skills in the above-mentioned domains.