jacobstephenson February 28, 2024

Machine Learning Operations

With the advent of new Artificial Intelligence being released over the last couple of years, companies are beginning to accelerate the production of data science models. What used to be sequestered to companies with niche data strategies is now diffusing to the wider ecosystem. Companies are investing in platforms, processes and methodologies, feature stores, machine learning operations systems, and other tools to increase productivity and deployment rates. MLOps systems monitor the status of machine learning models and detect whether they are still predicting accurately If they’re not, the models might need to be retrained with new data - I will go into more detail on how companies use machine learning in another post. Many of these capabilities come from agencies or external vendors who provide a platform for other, more nascent companies to train and deploy machine learning models. However, some organisations are now developing their own platforms. Although automation is going a long way to bolster productivity and provide broader data science participation, the most notable achievement here is that companies are able to reuse and leverage existing methodologies, data sets, and even entire models.