reproducability

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]

I’m going to give you a high overview of kubernetes and how you can make your R work shine in kubernetes. Are you, an R-user in a company that uses kubernetes? building R applications (models that do predictions, shiny applications, APIs)? curious about this whole kubernetes thing that your coworkers are talking about? somewhat afraid? Then I have the post for you! Many R users come from an academic background, statistics and social sciences. [Read More]