MT
You've shipped a model that looks strong on validation, but the team is not convinced it will hold up once it is used by real users. You are asked to explain how you would judge whether the model is reliable enough to trust.
How do you ensure that your machine learning models are robust and reliable?
You've shipped a model that looks strong on validation, but the team is not convinced it will hold up once it is used by real users. You are asked to explain how you would judge whether the model is reliable enough to trust.
How do you ensure that your machine learning models are robust and reliable?
