The Top 8 Machine Learning Trends of 2023

Machine learning (ML) algorithms are already commonplace in the business world, fueled by the extreme amounts of data now available and the advent of deep learning and artificial neural networks. We asked our in-house AI and data analytics experts what trends will be most prevalent in machine learning this year. Here are the 8 trends they identified.


1) Rise of MLOps

MLOps improves model accuracy and efficiency and involves a few teams – DevOps, IT, and data science – working together to solve any issues that may arise and implement best practices.

ML models begin degrading from the moment they’re implemented, which is called model degradation. MLOps helps to counteract model degradation, using continuous learning to ensure that models are accurate and up-to-date.


2) Data Governance

As the popular phrase “garbage in, garbage out” illustrates, an ML model’s accuracy is hugely affected by the quality of data run through it. And the increasing maturity of AI and ML has made data more powerful, raising the importance of a system for handling it. So, data governance is emerging as a “trend” – although it’s probably here to stay.

Data governance can also help with the increase of data privacy regulations, such as the EU’s GDPR.


3) Use of Third-Party Data

Companies are increasingly using third-party data in addition to their internal data to avoid having gaps in their market understanding.


4) No- and Low-Code ML Platforms

No- and Low-Code ML Platforms allow users to drag and drop algorithms to build quality, vetted models in just a few clicks. No- and low-code platforms open up the world of ML to companies challenged by hiring tech- and AI-skilled workforces and help alleviate the heavy workloads of data scientists.


5) Microservices

As of 2020, 46% of organizations were developing or migrating at least a quarter of their systems to microservices, according to O’Reilly. That migration isn’t going anywhere.


6) ML in Embedded Analytics

Embedded analytics is when data analysis takes place in a user’s usual platform or workflow, so they don’t have to navigate away from the platform to seek out up-to-date data analysis.


7) Domain-Specific ML

ML can be deployed in any domain imaginable. However, the ML models a data scientist would implement vary depending on the use case and domain. This is where domain-specific ML comes in. When factored into the process of developing ML models, domain knowledge and subject matter expertise save time and require fewer iterations.

Sometimes it’s critical to build domain knowledge into an ML model, which was the case when a medical research organization (MRO) wanted to use computer vision to score Clock Drawing Tests (CDTs), which screen for Alzheimer’s and other forms of dementia. Doctors’ domain knowledge had to be built into the model to teach it to score and diagnose CDTs. Learn more about this use case and its impacts here.


8) Multi-Modal Learning

Multi-modal learning is an emerging type of ML that can simultaneously process information from multiple modalities. An example would be a model that could simultaneously process information from visual and auditory modalities using a combination of Natural Language Processing and computer vision.  


Learn more and discover real-world use cases for these ML trends by reading our experts’ eBook, Top Projected Trends in Machine Learning for 2023 here.

Leah Moore
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