Role of Machine Learning in IoT
In this series of IoT articles on our website, I have lost count of the times I have mentioned that when we combine IoT with some powerful artificial intelligence and machine learning algorithms, it is capable of some pretty high-level stuff.
This is why I have dedicated an entire article to discussing the role of machine learning in IoT. In this article, we will go over various intersecting and fascinating topics like what machine learning is, why we must use ML in IoT, a few benefits of using ML in IoT, and a few more miscellaneous topics. So sit down, pay attention, and quench your curiosity.
What is Machine Learning?
To define machine learning crudely, it is a field of understanding and building methods that ‘learn’ using acquired, stored, and analysed data. With the help of data, machines tend to improve and learn to perform better.
Machine learning algorithms analyze the inputs given and the outputs obtained to create a generalized solution to predict outputs for new inputs. If I have to talk about machine learning, I can go on and on forever.
This vast topic includes various types of learning, for example, supervised learning, unsupervised learning, and reinforcement learning. Moreover, each category has hundreds of algorithms like KNN, classification, regression, lasso, SVM, Naive Bayes, and countless more.
What is the Market Size of Machine Learning and IoT?
There are countless articles and reports on the net with various predictions about the number of IoT devices. Some say that there will be over 21 billion IoT devices by 2025. Others say there will be 60 billion IoT devices by the end of 2027. These predictions come in all shapes and sizes, by the fact is certain: IoT is inevitable, and it will continue growing.
In terms of money, the IoT market has grown to 157.9 Billion USD with a CAGR of 22.4%. It is expected to expand up to 525 Billion USD by 2027. Similarly, Machine learning has a market size of 15.66 Billion USD and is expected to go up to 21 Billion USD in 2022.
What are the Benefits of Using Machine Learning in IoT?
Since machine learning is a key component of Software AG’s Cumilocity IoT low-code, self-service IoT platform, it comes ready to go with various tools you need for fast results.
These results include device connectivity and management, application enablement and integration, machine learning, streaming analytics, and machine learning model deployment.
Before we jump into the applications of machine learning in IoT, let us take a moment to discuss the advantages of using machine learning in IoT.
1. Simplifies Machine Learning Model Training
One of the main advantages of using machine learning in IoT is to help you quickly build new machine-learning models easily.
For example, AutoML support allows the right machine-learning model to be chosen based on your data.
This data can either be operational device data captured on the Cumulocity IoT platform or historical data stored in big data archives.
2. Give you the Flexibility to Data Science Library of your Choice
There are countless data science libraries available for developing machine learning models. Some examples are Keras, Tensorflow, and Scikit-learn.
Even Cumulocity IoT machine learning allows you to develop models in data science frameworks of your choice. Furthermore, these machine-learning models can be made available for scoring within Cumulocity IoT and transformed into industry-standard formats using open-source tools.
3. Rapid Model Development
No matter which data science frameworks you use, model deployment into production environments is possible in one click, either in the cloud or at the edge.
Machine learning models can be monitored and updated without any hassle. Moreover, verified and pertained models are available for immediate deployment to accelerate adoption.
4. Pre-Built Connectors
Incorporating machine learning into IoT provides easy access to data residing in operational and historical data stores for model training as it can retrieve this data periodically and route it via an automated pipeline to transform the data and train and test a machine learning model.
This data can be hosted on cloud platform services like Amazon S3, Microsoft Azure, Data Lake storage, and other local data storage. In addition, when using Cumulocity machine learning and IoT, you can even retrieve this data by using prebuilt Cumulocity IoT DataHub connectors.
5. Integrating with IoT Streaming Analytics
When you pair machine learning with IoT, it enables high-performance scoring of real-time IoT data. For example, Cumulocity IoT Streaming Analytics provides a “Machine Learning” building block in its visual analytics builder. This allows the user to invoke a specified machine-learning model to score real-time data.
6. Notebook Integration
Numerous data science library notebooks provide an interactive environment across programming languages. Such notebooks can prepare and process data, and train, deploy, and validate machine learning models.
7. Waste Reduction
We all know that machine learning is very efficient on its own already. Pair it with the ever-powerful IoT, and it helps in improving the operational efficiency of businesses as it cuts down waste and other unused resources.
8. ML adds visibility to the supply chain
IoT implementation has provided the most favourable support in supply chain management. For example, The IoT sensors used in vehicles and shipping containers provide critical details such as the quality of products. This real-time data brings visibility to the supply chain and also provides more scalability.
Applications of Machine Learning in IoT
Now we know the advantages and benefits of using machine learning in IoT. But first, let us look at some of ML’s applications.
1. IoT and ML in Agriculture
They say that in the future, only two professions will remain, agriculture and teaching — one for physical starvation and the other for mental starvation. We have already seen numerous applications of IoT in agriculture.
The usage of machine learning and IoT in the agriculture industry is expected to increase rapidly. In the future, the interaction between the farmer and agriculture process will be done using the data generated from Machine learning and IoT.
2. ML and IoT in Healthcare
Needless to say, the Healthcare industries have started using technologies that help fulfil the healthcare facility, in-house diagnostics facility, and disease prediction tools built with IoT and Machine learning.
We have already seen countless examples of IoT in the medical sector, including wearable devices and monitoring tools for patients to provide heads-ups to patients and doctors.
However, machine learning, on the other hand, offers technology to get medical records and integrated data extracted with the help of IoT.
3. Machine Learning in IIoT
We have already seen that IIoT (Industrial Internet Of Things) uses IoT sensors and devices to enhance manufacturing and industrial processes.
Machine learning is used in Industry 4.0 to help in enterprise resource management, maintenance, and automated industrial processes.
4. Enterprise Resource Management
Before we look at how ML and IoT are used in enterprise resource management, let us first look at what it is. Enterprise resource management revolves around resource management, supply chain management, work management, and health and safety initiatives.
Business owners use IoT and ML mainly to encounter problems by providing real-time solutions to enterprises. Other benefits include a highly responsive environment (ecosystem) and an increment in operational efficiency.
These are only a few applications of machine learning. There are countless examples of machine learning and IoT in the real world. All you have to do is explore!
Summary
You have now learned what machine learning is, the market sizes of machine learning and IoT, the various benefits of using ML in IoT, and a few real-world applications.