Ask HN: What is your production ML stack like? (2021)

I'm curious about your ML stack that is also used in production. What has failed, what has given joy?

Have you managed to set up a reliable "MLOps" environment with a small(!) team? What are the ingredients?

To what extent do you monitor your model inference performance? Is there an automated KPI tracking in place to make sure the new model architecture or a new set of weights perform as expected?

How much of your deployment has moved to an "ML Cloud"? Whether it's an AWS, GCP or Azure ML-specific services. Which are the ingredients?