Ask HN: What does Machine Learning production look like in your case?

[dpleban]

Hey HN! I'm working on a talk/blog on a related topic and was curious to see what the case is for people here. I think each case is a bit different so I don't want to constrain people too much.

If you deploy ML to production in your group/team/company – what does production mean for you?

Examples: - "We run a model once a week that predicts some stuff and stores it in a table, then the customer queries it" - "We create an inference endpoint on some cloud resource, which our product/users use to predict poses in videos" - "I wish I knew, we're still figuring it out" - "We deploy a model as part of a larger pipeline in a system of microservices (and other buzzwords)"

Also, if you are in an extra-sharing mood – in your version of production, were there any counter-intuitive things you learned when you first set up the pipeline?

Cheers! Enjoy the picture Dall-E2 made for you of a cat asking for upvotes in return. https://labs.openai.com/s/2enTplV9c9OxU7lyqhyIjXlN