Are you curious how and why the docker engine is using layers during the build time? Updated the new Docker 🐳 and Python 🐍 tutorial with a short explanation about the image layers ⬇️
I got to attend the Data Science Education Community of Practice (which is connected to the American Physical Society) conference this week at the University of Maryland. I learned a lot! #datascience#iteachphysics@edutooters
hey #genomics / biomedical friends! we are looking for a second reviewer to review biocypher - a #python package that provides a framework for creating biomedical knowledge graphs - the wonderful @arianemsasso is editor for this one. please repost!! #science#datascience#openscience
Pezzo is an #opensource platform that provides tools for prompt engineering at scale. That includes features for managing prompts, creating workflows, deploying, and observability.
It supports prompts with tools such as #chatGPT, #LangChain, AI21labs, etc., and is based on node.js and runs on #Docker.
About fifty years after Smith's seminal map for the Geology of Britain and we see numerous 19th century British publishers including some version of it into their atlases, such as this from c. 1860 by Bradbury - Agnew. Hand colored to distinguish the strata.
Still a relatively young science at the time, but clearly making an impact, and unveiling an important key to understanding our world.
I didn't get an academic job (hence my radio silence: I've mostly been moping 😔 and brushing up on my python/R) and am currently looking at a pivot to data science/industry in general. I've got plans for learning the stuff I need to learn and I have a couple of bootcamp-y things lined up so I can get some more industry contacts and a line on the resume that looks vaguely like relevant experience.
If anyone has advice about this sort of thing, I'd love to chat!
Rule #12 of #datascience: be your own best competitor. If your ML model is your key differentiator, once you ship it start working on a new and improved version. #kcdc
K-Fold Cross Validation: re-split your training vs. test data a bunch of times (usually 5x) to see whether your model is valid or whether you wound up in one of the naturally-occurring clusters in random data. #datascience#kcdc
Have a favorite data science model, but try that in competition with another model.
Linear equations normally work well because you're either dealing with people or things that depend on people. But also check against a nonlinear model, and pick which one models the data better.
You can't add apples and oranges; match units on your calculations when doing data science. Normalize your units like you're back in grade school. #kcdc#datascience