Thread by @svpino

Learning about containers will open many doors for you.

If you are a Machine Learning Engineer, containerization is a must.

For the most part, "deploying machine learning" has a lot to do with containers.

There are a couple of ways you can approach this:

Understanding how containers work, building blocks, the standardized API, etc.

Or, you can start with Docker, find a few examples of how to use it, and progress from there.

I've been deploying things inside Docker containers for years now.

I'm sure I can't explain most of the things happening behind the scenes.

Anyone with that knowledge is in a much better position, for sure, but that doesn't mean that I can't get my work done correctly.

We all learn things differently. We all have a different way of approaching the field.

Find out what's the most effective way you can progress and go for it!