I have a machine learning model that takes some time to train. Data pre-processing and model fitting can take 15–20 minutes. That’s not so bad, but I also want to tune my model to make sure I’m using the best hyper-parameters. With 16 different combinations of hyperparameters and 5-fold cross-validation, my 20 minutes can become a day or more.
Locking down R package dependencies and versions is a solved problem, thanks to the easy-to-use renv package. System dependencies — those Linux packages that need to be installed to make certain R packages work — are a bit harder to manage.
I’ve set myself an ambitious goal of building a Kubernetes cluster out of a couple of Raspberry Pis. This is pretty far out of my sphere of knowledge, so I have a lot to learn. I’ll be writing some posts to publish my notes and journal my experience publicly. In this post I’ll go through the basics of Kubernetes, and how I hosted a Plumber API in a Kubernetes cluster on Google Cloud Platform.
It’s no secret that I love R and begrudgingly use Python. But there’s a another option for data science, and it promises the speed of C with the ease of use of R/Python. That language is Julia, and it’s a delight to use. I took some time to learn the basics, and I’m sharing my impressions here.