Where is Machine Learning Headed in The Year 2022?

Machine learning quickly went from a sci-fi dream come true to common and ubiquitous sight. As users become more attuned to smart technology and instant gratification, entrepreneurs are hard-pressed to deliver a constantly improving experience, in each and every use of ML. As a result, we are witnessing some evolutionary trends in machine learning, some intended to improve customer experiences, others to lower operational costs of the delivery of the solution.

Moreover, 2022 will remain a critical year, as the world recovers from the pandemic and the already booming ML use cases take a leap towards their next big phase. Let’s look at the top machine learning trends in 2022:

Low-code ML/AI

Low-code ML is a process of creating ML apps without excessive or hardcore coding. Rather than spending time on manual coding, developers can use customized frameworks to build software apps using the drag & drop interface rather than coding it from the bottom up. While some may argue ML is a little too complicated to implement, the trend is here already and is slowly becoming quite mainstream. Attributing to its ease of use, no-code machine learning is probably going to become popular in creating simple and easy apps that need quick development. 

TinyML

This is another breakthrough in the arena of machine learning. Tiny ML is inspired by the Internet of Things and the primary idea that supports it is enabling ML-based processes over connected edge devices as well as low power consuming devices. The “Hey Siri” phrase for iPhone is an example of TInyML. It makes ML more usable and versatile across use cases. TinyML is also expected to make machine learning more affordable with low power consumption patterns, lower latencies, lower bandwidth, etc. 

MLOps

Another exemplary trend in machine learning is MLOps. MLOps is a concept derived from DevOps and it includes some best practices that foster transparent, quick, and seamless working of ML operations as well as development. Given the challenges of ML, like scalability, complexities of ML pipeline development, sensitive and dynamic data management, team interactivity and collaboration, etc, MLOps is capable of solving them through standard practices of concurrent ML deployment. With MLOps, you have to pay close consideration to data collection and cleaning processes, along with the processes for model training as well as validation. Therefore, enterprises that are growing at an unprecedented rate will benefit the most from MLOps deployment.

Unsupervised ML

Supervised learning involves the preparation of data for processing beforehand for the ML model. In unsupervised learning, unlabelled raw data is fed to the system for it to independently analyze and use it. The data here is not categorized and is not divided into separate groups. The ML model can hence independently determine possible insights, various dependencies, or relationships according to its capability. This practice works well for tasks like computer vision, anomaly detection, medical imaging, customer persona definition, and content categorization on websites.

Single-shot learning 

One-shot learning involves learning with a single image only. It uses a Siamese network that contains two subnetworks that act as mirror images of one another. The subnetworks match a standard image for reference, with the image which needs identification. It then gives a similarity score as an output where the system defines if a new, individual image is similar to the image used for the reference. Such an approach is popularly used for use cases like facial recognition, biometrics, etc, which require comparison with a few images. 

Summary

ML with its evolution is shifting towards smarter automation, efficiency, and speed while becoming highly accessible too. As machine learning is a vital asset for companies of all scales, it is also becoming more affordable as well as versatile to benefit broader categories and sizes of businesses. Moving forward, we might therefore see much more exciting implementations of machine learning along with brand new ML approaches for modeling. To get started, get in touch with a machine learning development company.