Introduction

Data science blueprint is a nothing more than a data science project structure proposition. Its purpose is very simple :

  • help you build a robust project
  • help you gain some confidence regarding the reliability of the code
  • help you meet the requirements needed to deploy in a production environment
  • Bring data scientists and machine learning engineers to work together around a common code base

To reach this goal, the blueprint proposes some interesting features that will be covered in this documentation :

  • a personalized backbone for your data science project, thanks to cookiecutter
  • a dockerized environment that you can use to work with notebooks
  • a code quality focus, with the set of tools that will help you profiling and testing your code
  • a set of tools to let you use your project as an app that you can deploy on a production server, or on a remote python

repository like Pypi

The Data Science blueprint works with Python projects, with fully packaged code and also Jupyter notebooks. It is particularly adapted to data science projects.