programming

Data scientists, in general, do not just throw data into a model. They use feature engineering; transforming input data to make it easy for the chosen machine learning algorithm to pick up the subtleties in the data. Data scientists do this so the model can predict outcomes better. In the image below you see a transformation of data into numeric values with meaning. In this article I’ll discuss why we still need feature engineering (FE) in the age of Large language models, and what some best practices are. [Read More]

This is a post for people who only work alone or wonder why on earth you would use all those fancy tools like linting, unit-tests, and fancy editors. I hear you, why would I use all those extra steps? That sounds like busywork you do instead of actual work! I think you just don’t haven’t experienced development work like I have, and I would like to share how my work feels and looked like in the past few years. [Read More]