What is hardcore data science—in practice?

Via Ideas O’Reilly

Um bom artigo que fala a respeito da inserção de modelos de Data Science em produção, ou o que é mais conhecido como Core Machine Learning.

So far, we have focused on how systems typically look in production. There are variations in how far you want to go to make the production system really robust and efficient. Sometimes, it may suffice to directly deploy a model in Python, but the separation between the exploratory part and production part is usually there.

One of the big challenges you will face is how to organize the collaboration between data scientists and developers. “Data scientist” is still a somewhat new role, but the work they have to do differs enough from those of typical developers that you should expect some misunderstandings and difficulties in communication.

The work of data scientists is usually highly exploratory. Data science projects often start with a vague goal and some ideas of what kind of data is available and methods that could be used, but very often, you have to try out ideas and get insights into your data. Data scientists write a lot of code, but much of this code is there to test out ideas and is expected to not be part of the final solution.