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Could you please provide some examples of what a ML Ops person might do/manage in their day to day jobs?



- Tracking changes is different. Not only do you have to track code changes, but also training data and model changes. You need to build systems that allow for this change management.

- Monitoring -- you have to build specialized monitoring to check for model degradations. Is the model still outputting valid/correct predictions? If not you need to roll back to an old model (see above).

Those are the two main ones.


I would say this falls under #3 - ML engineer


Oh wow, those are actually fascinating problems! Thank you.




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