casino siteleri
BusinessComputers and Technology

Machine Learning Applied to IoT Data

Machine Learning Applied to IoT Data

The Internet of Things is a booming landscape– every day, we hear a new innovation coming through in the field of internet-connected devices and their promising future in transforming our lives. From controlling the temperature of our room to finding our parking spot in the crowded shopping complex, IoT-driven devices are truly changing the way we use and live with data.

Not long ago, we would ask humans about the way to a destination if we ever got lost. Not anymore… as we use applications like Google Maps to navigate through the roads and reach our destination.

Do you want another familiar example from real-life?

How about this —

“Hey, Alexa! Please play the song “Hotel California”.

Or using Google Assistant to call your best friend from the stored contact list!

Behind these wonderful applications, there are millions of data points and innumerable automated machine learning software packs running in sync. For the successful operation of any IoT device, what you really need is a Big Data-based machine learning platform.

Clearly, we are ahead of our times with Machine Learning applications that are controlling voice devices such as Alexa, Siri, or Google Assistant.

If you talk to a data scientist or a senior AI engineer, you are most likely to hear that AI and ML techniques are no longer novelty technologies. Rather, these are staples to make any engineering work on its own with an “Artificial” brain of its own, maybe even emotion– if we fast forward the timeline to the 2040s.

But, the future depends on how well Machine Learning developers and engineers work with IoT Data. IoT Data, a subset of Big Data, is collected from billions of devices that are connected on the internet– such as your smartphone, tablet, desktop, or the public Wi-Fi or hotspot.

Going a step further– if you play your Spotify in your car through Bluetooth or Wifi connectivity– you are generating Big Data via IoT pools. All these activate the data lake owned by IoT operators. These could be the local government, or your current telecom service provider.

AI and ML companies rely on data collected from these sources to predict, filter, and manipulate the final outcomes with ‘supervised’ paths. In case, where data is not controlled, ML models can still be used to control the outcomes through unsupervised learning along with reinforcement and cognitive intelligence techniques.

Unsupervised learning– that’s what you should focus your career on. Most top-ranked machine learning courses in India have taken forward the philosophy of automating Data Science applications with IoT-specific landscape in mind. Simply because IoT data is the most readily available data available today.

For more informative articles keep visiting Emu Article.

Alen Parker

Alen Parker is a critically-acclaimed writer who has generated a wide range of content during his professional career. His industry-wide experience into writing for different niches is certainly an admirable aspect that empowers him to create enriching, informative write-ups.

Related Articles

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to top button