- Who: In this story, there are three main characters: 1) the people/the community who needs help, 2) the data scientist (that is you), and 3) AI.
- How much does the data scientist understand Assignment 1 (domain) and Assignment 2 (data)?
- What models and analysis did the data scientist and AI apply to fulfill the need of the people or the community?
- Can the data scientist estimate and select data for their goals from Assignment 1? Can they map data sets from Assignment 2 onto appropriate ML models?
- Can the data scientist connect Story 1 with ML models/stories about what a ML model can do? To perform good ML research, what in-depth knowledge and experience with ML algorithms and ML stories does a data scientist need?
- When has to do with the iterations (Calibration 2). How much time did it take for experimentation? How efficient is the modeling/algorithm?
- Can the data scientist determine the acceptance level of the model (validation with accuracy and runtime performance) considering the targeted users?
- Where has to do with the learning environment. Where did this experiential learning process take place? For example, it was part of an online Deep Learning course.
- Why explains the modeling. Explainable ML models.
- How: If you would like, you can add a dimension of how. How did it happen? Sometimes, the answer to how can be covered by what, when and where.