Internet of Things devices sends vast amounts of data to each other and the Internet. In machine learning, data is used to make sense of things, and it does this by giving us new information about them. Machine learning looks at how people have done in the past to look for patterns and build models that can help predict what will happen in the future.
What is Machine Learning
Thanks to machine learning, computers can learn and improve, a branch of artificial intelligence (AI). Computers can learn and improve independently without having to be programmed and re-programmed. This makes machine learning different from other types of knowledge.
Machine learning is making computer programs that can use data and learn independently. Starting with data like examples, direct experience, or teaching helps us make better decisions based on what we’ve learned. Computers should be able to learn independently without being taught by humans.
Why use machine learning for IoT?
Machine learning can help people understand the hidden patterns in IoT data by analysing vast amounts of data with sophisticated algorithms. Thanks to machine learning inference, automated systems using statistically determined actions can enhance or replace manual procedures in essential activities.
Sample use cases
Companies use machine learning for IoT to predict what will happen in many situations, which gives the business new information and more advanced automation tools.
You can do these things with machine learning for IoT:
- Ingest and change data into a single format.
- Build a computer model that can learn about itself.
- This machine learning model should be used on the cloud, on edge, and on your phone or tablet to make it work.
For example, using machine learning, a corporation may automate quality inspection and defect tracking on its assembly line, track asset activities in the field, and estimate consumption and demand patterns, among other things.
What is IoT?
The Internet of Things (IoT) is a network of real-world things with sensors, software, and other technologies built in. It’s called the “Internet of Things.” Physically, what makes them different from the rest? What matters most is their capacity to communicate with other devices and systems via the internet and share information. The complexity ranges from simple household items to high-tech industrial tools.
Over 10 billion IoT devices will connect by 2022, and 22 billion are expected by 2025. People who make devices work with Oracle. Combining these different products and adding sensors gives them a level of digital intelligence that makes people’s lives easier. There is a push for devices that don’t need a person to show real-time data to each other.
Benefits of machine learning inference for IoT
A big part of our low-code, self-service platform for IoT is machine learning. Connecting and managing your devices, integrating apps, and deploying machine learning models are just a few things you can do with the platform.
Venue: The platform can be used on-site, in a cloud, and at the edge. With Cumulocity IoT, there are also edge-only solutions that can be used.
Simplify machine learning model training
You can quickly build new machine learning models with Cumulocity IoT Machine Learning. Using AutoML, you don’t have to think about which machine learning model is best for your data. You can use operational device data from the Cumulocity IoT platform or historical data from extensive data archives to determine which model is best for you.
Freedom in choosing a data science library of your liking
There are a lot of data science libraries you can use to make machine learning models, like Tensorflow, Keras, and Scikit-learn. Using Cumulocity IoT Machine Learning, you may build models in any data science framework. These models can be converted into formats used in the real world and made available for scoring in Cumulocity IoT.
Rapid model deployment to operationalise machine learning quickly
When a model is made in Cumulocity IoT Machine Learning or imported from another data science tool, it can be deployed to production environments in one click, either in the cloud or at the edge. Operationalised models can be easily checked and changed if the underlying patterns change. Also, models that have already been trained and tested can be used right away to speed up adoption.
Prebuilt connectors for operational & historical datastores
Cumulocity IoT Machine Learning makes it easy to get data from operational and historical data stores to train models. Can get this data from time to time and send it through an automated pipeline to change the data and train a machine learning model.
If you want to store your information on Amazon S3 or Microsoft Azure’s Data Lake Storage, you can use prebuilt Cumulocity IoT DataHub connectors. You can also keep your data on your computer and get it from there.
Integration with Cumulocity IoT Streaming Analytics
Cumulocity IoT Machine Learning lets Cumulocity IoT Streaming Analytics score real-time IoT data quickly. Cumulocity IoT Streaming Analytics includes a “Machine Learning” building block in its visual analytics builder to facilitate real-time data scoring, which allows the user to call upon a specified machine learning model to score the data in real-time.
This will enable you to connect machine learning models to streaming analytics workflows without writing code.
Notebook integration
Jupyter Notebook, a standard in data science, allows you to work with different programming languages in the same place. Use them to get and process data, train machine learning models, deploy them, and check them out. This open-source web app is connected to Cumulocity IoT Machine Learning, making it easier for people to use it.
How is machine learning used for IoT?
1. An intelligent home
People can use smart home devices to control their lights and TVs. These devices are called “smart homes.” IoT sensors, machine learning models and algorithms, and big data analytics are all part of the IoT platform. As a result of this integration, users will find their homes much more helpful and responsive.
People who use IoT platforms can help predict what will happen by collecting data in real-time rather than relying on commands and manually setting up routines.
2. Smart cars
As of now, self-driving cars are only being tested. They have already become a reality in the automobile industry. Utilising supervised machine learning models and algorithms, automakers can implement and monitor how vehicles respond to various circumstances and design cutting-edge driver-assistance systems.
Examples of when machine learning is used to drive a car:
- A proactive way to look for and classify objects
- Checking on the driver
- Replacement: The driver
- Putting together all of the different sensors
- The powertrain of a car
Machine learning mimics how the human brain processes information for artificial intelligence. If machines use knowledge, training, or experience, they need an algorithm. Machine learning seeks to recreate this self-regulating behaviour in devices.
3. Data analysis automation
When a car moves, many data points are recorded by built-in sensors. Passengers need to be safe and have a good time processing the data. It would be impossible to do this for each car, so the only way is to make the cars do it.
Using machine learning, a vehicle’s central computer system, analogous to a human’s central nervous system, may learn about potentially hazardous conditions, such as speed and friction characteristics and then activate safety systems when necessary.
4. The predictive power of Machine Learning
The value of IoT isn’t just spotting threats but finding commonalities. For example, a car sensor might learn about a driver who makes too tight turns or has difficulty parallel parking. The system starts to build itself into being able to help the driver.
In the Internet of Things, machine learning is different from other types of IoT because of how it works. The most important thing about machine learning for the Internet of Things is that it can recognise when something isn’t right and raise red flags. It gets better at being accurate and efficient as it learns more about a subject. Google’s heating and air-conditioning system is an excellent example of using less electricity and saving money.
Conclusion
Machine learning can be used to look at a lot of data. However, even though it is faster and more accurate than humans, it takes a lot of time and money to learn how to use it. The ability to process a lot of data improves when machine learning is used with AI and cognitive technologies.
About the Author
Nishanth is a Startup Specialist working at NeoITO – a reliable Software Development Company based in the USA. He is an avid reader and writer, and works closely with entrepreneurs to stay updated on the latest.
