- TinyML has the potential to run powerful learning models based on artificial neural networks;
- TinyML devices are set to reach 2.5 billion by 2030;
- TinyML is the best technology for performing on-device data analytics for vision, audio, and motion
, Technology has surpassed imagination and has come with fast-paced and wide-ranging value additions to our lives. The advent of technology has led us towards connected devices holding the ability to communicate and interact with each other and can transform premises of all types across industries.
Any guesses which technology is being discussed here?
Yes, you shot the right arrow, it is IoT. As we all know the very concept of IoT provides an ecosystem of connected devices. And indeed, it is the most significant era-defining technology in the current spectrum.
Today, IoT apps have expanded their reach not only for the daily tasks but also for the complex tasks involved in industrial production.
However, there is always a fear of excessive costs associated with the optimization of these apps. But what if I will tell you that you can optimize them while triggering the growth of your business, without draining you off financially?
You would not believe me? Right???
Well, hold your breath, because this is very much possible! And it is all due to TinyML.
TinyML; does it sound unusual to you???
Then I insist you read this post to unleash the facts further…
What is TinyML?
As the name suggests TinyML- Tiny machine learning is a miniature version of ML algorithms, that can be embedded within the IoT device. They perform the same ways the usual ML algorithms and help in making IoT apps energy efficient while improving data privacy and security.
That is amazing! Yes, it is, and there are many other benefits associated with it, that expedite the speed of IoT solution without making you lose millions on it.
Advantages that come along with TinyML
It is very likely in the coming years; our world will gradually turn into a nexus of devices. Hence implementing the IoT apps in our business models, is not a choice but vocal out the hour of the need. Currently, there are 250 billion microcontrollers in the world today and this number is growing by 30 billion annually.
It is the fact, and it is all possible because TinyML model consists of smallest architecture, that can fit into almost any environment.
Here, including TinyML within the IoT apps would benefit in certain ways, which are briefed out further in this post:
Energy efficiency gets enhanced
Machine Learning involves a complex ecosystem, which leaves behind the carbon footprint of AI technology. This further leads to a compromise in the accuracy and reliability of data. With the integration of TinyML, the algorithms used result in a decreased number of carbon footprint to a large extent. And worth to mention but it doesn’t let the algorithm’s reliability to get compromised at any given cost.
Provides relevant data
Data is one of the most vital assets for a business, but not every data collected is relevant. Henceforth, there needs to be a system that only picks the required and pertinent data, and this demand is well addressed by the TinyML. It can be programmed to collect data that fits the requirements set by the programmer.
Offers an additional layer of data privacy and security
IoT devices collect relevant data, which is sent further to servers located in the cloud, where it gets processed as per the business needs. In this journey, there are multiple places where data can be compromised and bring security issues. Here TinyML brings that additional layer of security and lets the security feature remains intact with every data processing.
Data sharing time gets reduced
Within an IoT device, one of the biggest concerns is to manage the time lag of sending and receiving the data. The TinyML creates an algorithm, which further removes the dependency of the device on the network speed, leading to a reduced time frame of sharing data to and for.
Yeah, you heard it right, there are a few challenges of integrating TinyML in the IoT devices. Some of them are mentioned herewith:
- Technical challenges exist to conquer the computing mechanism
- There lies a difference between deployment and execution within web-based and embedded technologies
In a nutshell
This was a synopsis of highly engaging futuristic technology-TinyML, that is gaining a strong foothold on the ground but is still in a very nascent stage. There are certain improvements to be made to help it support the complex machine learning models further.
However, with the advent of technology, it is expected that very soon, we shall begin to witness the mainstream adoption of TinyML within different industries.
On the other hand, if you are yet to get a shot of IoT apps on your business, then you must not delay it anymore.
Get in touch with us to include this avant-garde technology in your business model.