A Data Scientist’s Guide to Docker Containers

Author(s): Jonte Dancker

Originally published on Towards AI.

How to enable your ML model to run anywhere

For a ML model to be useful it needs to run somewhere. This somewhere is most likely not your local machine. A not-so-good model that runs in a production environment is better than a perfect model that never leaves your local machine.

However, the production machine is usually different from the one you developed the model on. So, you ship the model to the production machine, but somehow the model doesn’t work anymore. That’s weird, right? You tested everything on your local machine and it worked fine. You even wrote unit tests.

What happened? Most likely the production machine differs from your local machine. Perhaps it does not have all the needed dependencies installed to run your model. Perhaps installed dependencies are on a different version. There can be many reasons for this.

How can you solve this problem? One approach could be to exactly replicate the production machine. But that is very inflexible as for each new production machine you would need to build a local replica.

A much nicer approach is to use Docker containers.

Docker is a tool that helps us to create, manage, and run code and applications in containers. A container is a small isolated computing environment in which we… Read the full blog for free on Medium.

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Published via Towards AI