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...
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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...
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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...
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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.
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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...
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