This article is part of a multi-part series “How to dockerize [x]”. The previous article is “How to dockerize Python environments”. JupyterLab For the first miles of many Data Science projects JupyterLab is the way to go. You may also use cloud hosted services like Google Colab, Amazon SageMaker Studio Lab…

As a skilled Data Scientist or ML Engineer you probably switch between technologies, programming languages and libraries quite often because you want to solve the problems in the best possible way. However, switching contexts can be hard and time consuming. 🤔 The solution For me, the solution to this problem is using…

In Zeiten dynamischer Darlehenszinsen 🎢 ist es umso wichtiger einen klaren Kopf zu bewahren und die optimale Zinsbindung zu wählen. Und genau dabei hilft dir der ZinsAssistent! Welche Zinsbindung soll ich wählen? Von den Banken werden meistens Darlehen mit unterschiedlichen Zinsbindungen von etwa 10, 15, 20 oder 5 Jahren angeboten. …

Inspired by this article, I also wanted to “dockerize” my Python environments. Why dockerize? 🐳 The advantages of using Docker are: Reproducability: No “worked on my machine” problems. Isolation: No (negative) interferences between projects. Cleanliness: Keep your base system clean from any project specific stuff. Ease of use: You will not even realize…