From notebook to post on the DGX Spark
How a notebook on the DGX Spark becomes a post on this site, and why live HR records never leave the machine.
r&d note
02_ / content
I share it in case something here sparks an idea, or gets you past a problem you're stuck on.
How a notebook on the DGX Spark becomes a post on this site, and why live HR records never leave the machine.
r&d note
Every example on this site runs on synthetic data. This is how I generate it, and how I test that it behaves like a real organisation.
article
This page stands in for a post that is only writing: no data, no code and no figures, and nothing on it asks for them.
article
Installing a local model on the Spark, and drafting a first post with it from the notes on this site.
r&d note
Teaching a local model the site's house style with LoRA, and reading what its training curve shows.
r&d note
everything to read
Two HR extracts with no shared key. I score how alike the names are, pick a threshold, and check what lands just under it.
video, 6:40
Kaplan-Meier in a few lines of Python: how long people stay, and where the curve drops.
video, 4:12
A manager ID column is enough. I rebuild the hierarchy from a flat extract and draw it.
video, 5:05
Start dates and leave dates are all it takes to count who was employed on a given day.
video, 3:50
Whole months between two dates, worked out the same way in SQL, Python and Excel.
video, 4:35
Leavers over average headcount for a month, built up one step at a time.
video, 5:20
everything to watch
Pick a department and see how long its new starters stay, against the whole organisation.
tool
Pick a workforce question and compare the same query written in each language, side by side.
tool
Headcount, starters and leavers month by month, on the synthetic organisation.
dashboard
everything in the portfolio