Machine learning - which trends are about to emerge in 2022?

My dear readers,

While some businesses worldwide have certainly come out stronger during these global crises, many have not, but for nearly everyone advanced technologies have revolutionized the way we live and work. 2020 and 2021 made us realize that technology is potentially an advantageous savior and certainly an important guide during a crisis.?AI, machine learning, and associated technologies have the potential to resurrect traditional business models from total chaos to a highly streamlined, cost-friendly, and efficient workflow.

Two out of three CTOs reported that machine learning is being used in their organisations and it is also the most popular AI subset.

A number of machine learning tools and models are increasing in prominence and usage, with all those who are interested in the field likely to benefit form keeping on top of trends in this area:

• Unsupervised machine learning

One of the most intriguing types of machine learning is so-called unsupervised learning.?Without the need for human intervention, these algorithms are able to identify unseen patterns and data grouping.

• No-code machine learning

The ‘no-code’ movement is showing no sign of slowing down. Before the advent of no-code platforms, any new service or function would require a skilled developer or engineer.?Today, many ‘no-code’ machine learning platforms exist, including DataRobot, Clarifai and Teachable Machines, which support enterprises on their journey to implement machine learning in their businesses.

• Automated machine learning

Automated machine learning?(AutoML)?offers a major shift in how the vast majority of enterprises approach machine learning. As the need for talented machine learning experts has grown, demand has outpaced supply and led to the creation of tools that democratise access to machine learning.

• Machine Learning Operationalisation?Management

A Machine Learning Operationalisation Management (MLOps) trend is picking up speed. The goal of this approach is to ensure the efficiently of machine learning models in the deployment and maintenance stages.

• Reinforcement learning

This is an effective way for software to uncover the best possible path to take through directly experiencing a certain environment.

To remain competitive in the cut-throat world of business, it is imperative that organizations embrace and implement ML-powered solutions in their operations.