Deploying Data Science Tools: Part 1

Deploying data science tools is crucial for adding value beyond a developer's local machine. This guide explores methods for making code runnable anywhere, starting with sharing code via GitHub and highlighting its dependency management drawbacks for the user. It then introduces Python virtual environments as a way to isolate project dependencies, which still requires the user to manage the setup. The article concludes by presenting Docker and containerization as a superior solution that packages the application and its entire runtime environment into a portable container, simplifying deployment for the end-user.