MLflow is a platform for the “machine learning cycle”. It’s a suite of tools for managing models, with tracking of hyperparameters and metrics, a registry of models, and options for serving. It’s this last bit that I’m going to focus on today.
Machine learning models get stuck at the deployment stage all the time. This stuff is hard.
Drake is my new favourite R package.
I’m creating an R API wrapper around my state’s public transport service. To make life easier for the users, the responses from the API calls are parsed and returned as tibbles/data frames. To make life easier for me, I need to keep track of the API call behind each tibble. I do this by using the
tibble::new_tibble() function to attach metadata to the tibble as attributes, and creating a custom
print method to make the metadata visible.
The last few weeks have been all about R package development for me. First I was exploring GitHub actions with the lovely people at the rOpenSci OzUnconf, and then I was off to San Francisco to learn about Building Tidy Tools with the Wickham siblings. I’ve picked up a lot about package development, so I’m documenting some of trickier things that I’ve learnt.