When you ask interview questions like: How would you design the ML stack that can recommend a restaurant given their past restaurant going history? Most candida

When you ask interview questions like: How would you design the ML stack that can recommend a restaurant given their past restaurant going history? Most candidates don't get into the craziness of things like bias in datasets or calibration of probability output of models. They dive straight into embeddings(they have the freedom in the interview to design something the are comfortable with), which is important to the quality but not necessarily the most(or even the only) challenging part of what the team works on.