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Understanding and use python virtualenvs from Data Scientist perspective

Understanding and use python virtual environments from Data Scientist perspective

A Virtual Environment is a real good way to keep the dependencies required by different projects in separate places, by creating virtual Python environments for each of them. It solves the “Project X depends on version 1.x but, Project Y needs 4.x” dilemma, and keeps your global site-packages directory clean and manageable. For example, you can work on a project which requires matplotlib 1.5.3 while also maintaining a project which requires matplotlib 1.4.2.

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Deploy a Django application connected to Azure SQL using Docker and Azure App Service

In this post I would like to share my experience during the deployment phase of a Django App connected to SQL Azure database. This was the first time I used Azure to deploy my applications and I'd no idea which approach to use. I had 2 way:

  • VM with specified size and O.S. (IaaS)
  • Azure App Service (SaaS)

Well, considering that I didn't want manage the server as whole, I decided for the second one.

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