Docker For the Modern Data Scientists: 6 Concepts You Can’t Ignore in 2023

It touches on one of the most painful problems not just in data science and ML but in all of programming — sharing applications/scripts and making the darn things work on others’ machines as well. While Microsoft, Apple, and Linus Torvalds meant well when they released different operating systems, they inadvertently created the never-ending struggle for software compatibility. Linux, Windows, macOS — each has its own quirks and idiosyncrasies. And let’s not forget the variations in Python versions, library versions, and the unpredictable landscapes of GPU drivers in machine learning. Enter containers. While they have been around for a while to address this problem, it was with the release of Docker in 2013 that they gained immense popularity. Since then, Docker and its containers have become the go-to tools for sharing anything that runs with code. <a href="https://towardsdatascience.com/docker-for-the-modern-data-scientists-6-concepts-you-cant-ignore-in-2023-8c9477e1f4a5">Learn More</a>