wolterlw

Writing from the Ukrainian side of Galicia here, I would point out that Krakow is far from being the center of the region. Przemyśl or Zamosc lie much closer to the center if you include our side of Galicia where pierogis are widely popular as well. Matter of fact my family and many others here cook and eat them every week on Sunday, which I now hold as a near and dear tradition. There is something special about rolling and folding the dough together, boiling the pierogis, cooking the mushroom/onion sauce. Since this is a food cherished by both Poles and Ukrainians I find the name "galicean" a great fit as well, since it's a region shared between the two countries.

because 90% of the industry work is MLOps the pipeline usually goes 1. make a POC inside a Jupyter Notebook with some scrappy, data and off-the-shelf model, define metrics and train a baseline to see if the whole ML endeavour might even be worth it 2. Do error analysis, find better data, tune parameters, re-train to see if you can improve upon the baseline 3. Make the first deployment, setup data collection 4. Automate 2 as much as possible because data is ever changing and you want to try many more off-the-shelf models 5. Deploy new models and collect ever more feedback

4 and 5 are basically a while loop that never ends and that's mostly MLOps It still requires proper ML expertise, especially when things break tho

If this becomes something like huggingface for Computer Vision - awesome. For now, it's a shame model descriptions seem to often be lifted straight from README.

Having a more standardized description, something like model cards [1] (CV example [2]) would be hugely beneficial IMO.

[1] https://arxiv.org/pdf/1810.03993.pdf

[2] https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNso...