As data-science practitioners we eat machine learning for breakfast. On a day-to-day basis, we’re dealing with dozens of packages and libraries to get the most of the data we have. But do we really believe that consistently improving a model’s accuracy is our final horizon? Of course not! Getting into production and staying in production should be our mission. In this white paper, you can also discover :
- When changing anything changes everything
- You better get prepared for data changes
- Models get old—keep an eye on them
- Data preparation and “the kitchen sink”
