Changing assumptions and ever-changing data mean the work doesn’t end after deploying machine learning models to production. These best practices keep complex models reliable.

The final dataset that can be utilised for model training and testing is the result of the data pre-processing procedure. In machine learning, a variety of methods like normalization, aggregation, ...

With Machine Learning (ML) the quality of data used to build predictive models heavily influences those model’s accuracy making the role of data in ML extremely important.

SEMdeep train and validate a custom (or data-driven) structural equation model (SEM) using layer-wise deep neural networks (DNNs) or node-wise machine learning (ML) algorithms. SEMdeep comes with the ...

In many applications of machine learning (ML), updates are performed with the goal of enhancing model performance. However, current practices for updating models rely solely on isolated, aggregate ...