Data Science

Introduction This document outlines an AIOps Maturity Model to help organizations assess and improve their Machine Learning Operations capabilities. It came from my own frustration that there weren’t any models that fit the real experience of end-to-end data science and operations relationships that covered both ‘conventional’ ML, and practically discussing LLM based systems and how completly differently you have to think about them. This was originally published internally around May ‘24 and then presented at NIDC as an ‘Eye Test Model’, and I promised that I’d eventualy publish it; this is it, dusted off and tidied up for public consumption. ...

This is another one of those “I kept googling the same thing over and over again” things that needed a post, except this time I made an issue to make a post and then started to repeatedly refer to that. TL;DR When you want to spin up an experimental environment and get it tied in to your Jupyter environment of choice (I actually quite like JupyterLab Desktop these days…), you need two steps. ...

Messing around with Elections NI data Sources: Live Data (for 2023) 2022 Assembly Elections Creating your own Google Sheet and referencing the crowdsourced data The above linked spreadsheets are naturally not editable by everyone; this is great for reliable data but isn’t so great when you want to make pretty graphs. Google Sheets supports the live referencing of external sheets in your own sheets, so you can ‘import’ the data from the read-only sheets as they evolve over the count, and then reference those data in your own visualisations. ...