Machine learning trends to watch out for in 2022

The impact of machine learning can already be found in companies across virtually all industries. A recent survey by software development firm STX Next discovered that not only have two out of three CTOs reported that machine learning is being used in their organisations but it is also the most popular AI subset.

As we enter the New Year, 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. Thanks to its ability to find similarities in data, unsupervised learning can be an extremely powerful tool for those businesses who are looking for cross-selling plans and improve customer segmentation.

The cluster analysis method is a popular data mining technique to find data grouping, with a range of forms of unsupervised learning including K-means clustering and hierarchical clustering also being used by data scientists.

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.

Instead of needing complex coding, users can easily create tools through an intuitive drag-and-drop visual interface. This approach saves time and money compared to normal engineer-led code writing, thanks to not requiring people who have hard to find technical skills.

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.

By automating traditionally manual processes, such as data labelling, not only can virtually anyone use this innovation but human error is also reduced. In practice, almost every stage of the machine learning journey can be automated, all the way up to deployment.

Machine Learning Operationalisation Management

Following the success of the DevOps methodology, 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.

These practices help create an environment where data scientists and operations are able to collaborate as effectively as possible. Communication has always been a weak spot that limits the success of machine learning projects, meaning even a small improvement in this area can result in significant progress on slow moving initiatives.

According to a report from Neuromation, the MLOps market is on course to grow from around $23.2 billion in 2019 to reach $126 billion by 2025, thanks to the strong interest in this field.

Reinforcement learning

Defined by Deepsense.ai as “the training of machine learning models to make a sequence of decisions”, reinforcement learning is an effective way for software to uncover the best possible path to take through directly experiencing a certain environment. By employing a reward and punishment system, the machine learns by trying all potential paths and selecting the one that offers the most rewards and therefore solves the problem as easily as possible.