As a both devops and ML professional at my job (it's a small company), I viscerally cringe at the same kurtosis for this company (which I just learned about). There could be some kind of latent jealously/projection causing that cringe, but here's my rationale (could be very specific to my career):

* Devops folks don't seem to tend to like math and often got there by practicing, "computers, IT, having fun hooking things together and getting them running."

* Data science folks don't tend to like devops and prefer to bash around on a jupyter notebook that's already given to them and then maybe extract that python and see if it runs, but they tend to come from more of a science background and got into python as a hobby or incidentally. They do not like bashing around and getting things running.

So now this company is combining a math term that has a specific meaning to an ML ops space, which is going to cause confusion.

Different sets of data can have different kurtosis measures. Sets can be platykurtic (flat gaussian curve or high kurtosis) or leptokurtic (tall gaussian curve, low kurtosis).

Now this company is coming in and telling a bunch of devops people, "Kurtosis means helm but automatically migrate data too." So they are applying the idea of, "leptokurtic deployments," presumably with the metric being, variation between the code and data parameters on those servers. Data science people who are told about it from devops people are going to initially hear, "somehow dealing with cleaning the data, like an ETL pipeline, perhaps an Airflow with data cleaning tools built in or something."

It's very confusing and not helpful to customers, I hate it. There are going to be meetings where ML/Devops people are very confused.

Naming is hard though -- but I wish they would have gone with something like, "platypus" and just have a cute little platypus baby as the logo and say, "yeah we liked the word platykurtic because we like making things regular and platykurtic sounds like platypus."