Ask HN: What does your Machine Learning architecture look like?

Curious about the different ways people are running their models in production. Theres important tradeoffs to be made between technology that is suitable for experimentation which is flexible and easy to iterate on vs running models in production where performance is critical. Feel free to tack on the size of your team/data and plug your work.