Q: 6
Your team is working on an NLP model and has just operationalized the first model. Your team makes
updates to the model, overwrites the original model, and puts this new model into operation.
However, one of the teams using the model has seen a decrease in performance and is asking to use
the original model.
What critical error did your team make?
Options
Discussion
B Quick question though: if the requirement had been to automate reverting to previous models, would C make more sense?
Its B, versioning is critical here. If you can't roll back, teams get stuck with bad updates.
Maybe D here. If the team didn't properly iterate, they could've missed catching performance drop-offs before rollout. Iteration is key in ML project cycles. Not totally sure though since versioning (B) kinda overlaps.
Be respectful. No spam.
