A recurrent question in many data science blogs is “hey guys, what machine would your recommend to do data science?”.The question is very interesting, as there isn’t a single answer to it. For example, if you plan to work in a cloud environment (note that in most cases, this will end up costing much more
Learn more about data wrangling and how we position it within the data value cycle.
With 53-90 of project failure, chances are each of us will be confronted to it. If making mistakes is part of the journey, let’s at least learn how to avoid some common ones.
A perfect model is often an illusion because of the ratio effort / impact, Model Quality Decay and Prediction Window. To be pragmatic, we should focus on models that are quick to update and implement.