The hidden costs of bringing models to production

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” 
Nicolas Gaude

About the author

Nicolas Gaude

Chief Technical Officer & Co-founder