In this tutorial, we will learn reproducible environments by Azure Machine Learning without even knowing how to create Dockerfiles.
A few years ago, Docker started gaining popularity. Everyone claimed that this tool was saving them incredible amounts of time. The only thing that you needed for solving the messy process of building, deploying, and managing apps was Docker. It was like heaven.
But… such a wonderful thing could be real? Well, it turns out that everything was true. A little part of paradise might mistakenly have fallen on earth?. Might be, or might be not but, because of that, nowadays if you are forced to choose the star player for replicable environments your answer will definitively start with a “D” and ends with “ocker”.
Besides, as you already might know, reproducibility is a must in any professional machine learning project. The problem is that achieving this isn’t always something easy to accomplish.
Data scientists love working on their local machines, and learning Docker, might distract them from their principal task: pip installing everything.
If you are one of those, I’m not here just to criticize you. I’m coming with a solution. The people that developed Azure Machine Learning knew that making replicable Python environments was a goldmine for capturing data scientists. They came with a solution to handle environments just inside your Jupyter notebooks.
Following the second video about Docker basics, in this video, I explain Docker architecture and explain the different building blocks of the docker engine; docker client, API, Docker Daemon. I also explain what a docker registry is and I finish the video with a demo explaining and illustrating how to use Docker hub.
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This entry-level guide will tell you why and how to Dockerize your WordPress projects.