I have learned three things over time: 1) Just because an excuse is correct doesn’t make it any less of an excuse 2) More value is added to a discussion by asking the right questions.
Is historical data available for building a data science solution? Does data attest to the business problem? In other words, is there a metric that highlights the business problem? I will go slightly off track.
Initially, I thought I would be safe not suggesting an effort distribution across various phases of a data science project. But then what good is an author who looks for such kind of safety nets?.
Looking at data (observations, variables, datasets) day in and day out makes me quite data-centric. So much so that when I got introduced to my colleagues on the floor at my new job, I requested.
To answer the question, we have to look at the process of Generative AI from farm to fork. You would say “farm to fork” is used only for food processing! Who stops us from experimenting.