What’s the best machine for data science? 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,
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.