Trends 2022: small data and edge computing

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Learn about another of the trends expected for 2022, including small data and edge computing, and why it is beneficial for your business.
Having large amounts of data is not always advantageous, as it can overwhelm businesses and also presents significant demands for its management and use. For example, big data requires large machine learning algorithms to process the data and when working with cloud-based systems with unlimited bandwidth, this is not a problem. However, when you need or want to work directly at the edge, it can be inconvenient.


One of the trends for 2022 is not to allow all data to zalo database in the Cloud or in the data center itself, but instead to work on it where it is collected, that is, on edge computing devices. This is why we are increasingly talking about “ small data ”: this concept emerged as a new model “to facilitate rapid cognitive analysis of the most vital data in situations where time, bandwidth or energy expenditure are essential.”



Prepare your company for the future: The technological trends that are coming


TinyML: Tiny Machine Learning
The concept of small data goes hand in hand with that of TinyML / small machine learning – which refers to machine learning at the edge, that is, machine learning algorithms designed to take up as little space as possible so that they can run on low-power hardware commonly deployed at the edge of networks.

TinyML is a technique that integrates reduced and optimized machine learning applications that require "full stack" solutions (hardware, system, software and applications) and that allow automated data analysis to be performed on the device at the very edge.

In other words, the technique is still ML , but with less energy requirements, lower costs and without an Internet connection. Under this concept, machine learning models are produced and implemented that are small enough to facilitate data management at the edge.





Around 2.5 billion devices are expected to come to market using TinyML techniques, with the main benefit of creating smarter and cheaper Internet of Things (IoT) devices.

Source: prnewswire.com



The report says that the sectors that will drive the need for TinyML chipsets will be manufacturing, smart cities and consumer applications.

There are several important benefits to using ML programs on edge devices :

Greater data security and privacy (since there is no need to transfer information to external environments)

Lower bandwidth demand

Lower latency (since there is no data transmission)

Operation independent of the Web connection


Another benefit is low power consumption: TinyML operates on microcontrollers or microprocessors that consume much less power than GPUs and CPUs, allowing devices to run on batteries for weeks or even months. Additionally, this model allows for more efficient data collection , as IoT devices enabled by TinyML technology can be programmed to collect only the relevant data.

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