Using Uncertainty to Monitor ML Models

In recent times, attention has started to shift from building machine learning models to deploying and maintaining them, which led to the growing interest for ML Ops. A crucial component in maintaining an ML model is monitoring: how do you know when it is time to retrain your model in... [Read More]

Make-fu - Setting up a makefile for a Python project

There’s a common saying among computer scientists that “A good programmer is a lazy programmer”. Coincidentally, there’s also a common saying among mathematicians that “A good mathematician is a lazy mathematician”. Being both a programmer and a mathematician I must be doubly lazy, and thus I often find myself spending... [Read More]

Python tidbits - The mysterious behaviour of int

When working with datatypes in Python I keep getting surprised by their intricate dynamic nature. When working with integer values in other programming languages we often have to declare the precision of the types that we are using. In the case of integers, we have a wide variety of ranges... [Read More]

Graph Convolutional Neural Networks

As more and more businesses strive toward becoming data-driven, the use of graph methods for storing relational data has been on the rise ( [1], [2], [3]). Along with these graph databases comes more opportunities for analysing the data, including the use of predictive machine learning models on graphs. [Read More]

Doubt - Bringing back uncertainty to ML

I have previously been exploring uncertainty measures that we can build into our machine learning models, making it easier to see whether a concrete prediction can be trusted or not. This involved confidence intervals for datasets and prediction intervals for models; see the previous posts in this series for a... [Read More]