I recently posted my personal high level thoughts about top digital trends in 2023. In this article I would like to expand on the second trend: Productionisation of machine learning systems and adaptive machine learning.
Over the last few years, companies learned to better find AI opportunities and develop initial AI products and proof of concepts. However, many of these products are still not productionised and value is not realised by the businesses. There will be a further push for productionization of data and AI within companies. This will involve creating seamless MLOps processes for managing data and deploying machine learning systems in production. This will help to ensure that the real value of these models is realised and integrated into the core business processes of a company, as well as machine learning systems are tested, with right level of governance .
All major cloud providers have already built their version of MLOps products. While they will still evolve, companies will start utilising their cloud products even more.
Another value drain is sustaining the AI product after it’s been deployed. It’s common to see perfornace deterioration in production. Adaptive Machine learning - the ability to change behavior after deployment and retrain as needed will enable machine learning systems to adapt to change in environment (such as consumer, political) and help businesses to make better decisions.
Adaptive machine learning algorithms will be better suited to detect changes in the data (such as data drift) and adjust the model accordingly. They can do this by constantly monitoring the performance of the model and data distribution and making updates based on new data.
This article reflects my personal views and opinions only, which may be different from the companies and employers that I am associated with.