Actually, your blog has been on fire lately!

I am biased, because I mainly use Python, but the BQ sdk is pretty nice and you can read from sqlite with Python and ingest rows in bulk using the sdk.

I was trying to research how prevalent remote Jupyter kernels are, but could only find a few open source projects (ie. something called kernel gateway - anyone using it?)

I have a very poor understanding of concurrency in general and more specifically a poor understanding of the kinds of Heisenbugs that will now be foisted on potentially unsuspecting Python users.

This means that if you max out memory by say loading a dataset that's larger than what you have capacity for, you crash the kernel and potentially also lose any code changes in your notebook that hadn't been written to disk.

I am honestly curious to learn what is the concrete end product in this vision/plan.

After my inevitable rejection, I asked for feedback from the interview and I wish I hadn't, because the letter that was sent stated that I simply did not have the abilities or talent to become a scientist.

I can laugh about it now, but at 15 and aspiring to become a chemist, this was devastating.

I suppose one could argue that if I was really into chemistry, I could have maybe come across this knowledge myself in my extracurricular studying, but the interview questions can be pretty much anything and the field is huuuuuge!

I was baffled, because even though I was at a good local school, this kind of knowledge was in the first year university curriculum in my country. Only later did I find out, that many top schools that send students to top research unis have years of interview prep.

Many years ago I was in this position and got an interview to study a science course at one of the top unis. When I went to the interview, I was given a picture of a molecule and asked to sketch a graph of the signals this molecule would give if run through a particular type of spectroscopy.

Many k8s benefits have to do with horizontal scaling. With many services you can deal with increased load by having more replicas. Same with ML inference but ML training doesn't scale horizontally in the same way.

I've probably ranted about this elsewhere but for many ML teams, container image is the wrong abstraction unit.

ML containers can be truly gargantuan in size and restarting them is not as cheap as with more lightweight containers for web services for ex.