How Does Machine Learning Apply To IoT Data
By 2025, there will be around 55 billion IoT devices! Sounds a lot? Well, try figuring out how many do we have now?
How Does Machine Learning Apply To IoT Data
IoT stands for the Internet Of Things. But it just doesn’t mean that. It’s the connection, the sync, and the technology that will revolutionize the future in a structured way. By 2025, there will be around 55 billion IoT devices! Sounds a lot? Well, try figuring out how many do we have now?
Machine Learning, on the other hand, is used in the detection and prediction stage. A lot of IoT platforms are now using machine learning to their advantage. Notably, Amazon AWS, Azure IoT from Microsoft, and Google’s Cloud IoT Edge are some of the key players.
IoT connects devices and collects data to keep the sync but how can machine learning be put to use? Well, if your laptop detects a virus, it triggers an alert. With IoT, It will send the virus report to the antivirus framework to the data collection center. And from there, Machine Learning takes on its course. It reads and analyses data and can predict future damages! It draws insight into the data collected.
Connectivity and seamless remote access are some of the most important features people look for in the booming technology world. Consumers must also feel safe about their data. With artificial intelligence powering IoT, it can transform its data into the rightful repositories and embed them across the entire ecosystem of devices, services, and data centers in the cloud.
IoT is the combination of what you would call an old set of tech tools. But, just with the right tools comes the perfect software to accommodate and analyze data.
They are made of:
- Mechanical parts and some electrical parts.
- The software to encompass the data and processors, storage, and sensors.
- Ports and Antennas.
- Analytics software to run new and old AI models.
How is the data we are looking at going to bring changes, and with what kind of data exactly?
The data found by IoT, usually called IoT data, is of various types:
- Metadata: All the classic information stating ID and class type.
- State Information: Data that describes the current status of the environment.
- Telemetry: Displays the telemetrics of the particular information from a particular set of software or just one.
- Commands: Actions performed will be recorded for future reference.
- Operational Information: The performance indicator displays, but this one records it for further information.
Connecting IoT and Machine Learning
We're taking this example, but regardless, it stays the same with the core concepts regarding any other framework.
- The sensors with IoT look for discrete variables such as vibration, noise, heat, and temperature. This data is then uploaded to the cloud for analytics.
- Now Machine Learning comes into the picture. The Machine Learning model sits on the cloud platform feeding on incoming data.
- The Machine Learning model splits the information into data used for training and verification.
- The model looks at hundreds of thousands of records for anomalies, correlations, and projections to develop a hypothesis.
- Once the hypothesis has been created, it needs to be tested and validated.
- Once a model has been validated, it's published as an executable endpoint. Next, the live streaming data can be passed through the trained model and make an inference about the machinery's status/health based upon what it already knows and has been trained to look for.
Machine Learning and IoT have been an important part of recent success in the corporate world. Facebook uses this in the form of DeepText, a text understanding engine, to automatically understand and interpret the content and emotional sentiment of the thousands of posts (in multiple languages) that its users publish every second.
Amazon started its brick-and-mortar store, aka grocery stores with IoT and ML enhancing the store in every possible way. It's called Amazon Go! It anticipates what you want to buy and it also tracks what you buy and tracks what you returned all in real-time! What you pick up and what you drop, again, all in real-time!
We can all agree on the statement:
By 2025, there will be around 55 billion IoT devices! And that's not going to be the end. With IoT and Machine Learning, we can reach new heights and explore new places!