A Guide to MLOps
mlops.swiss-ai-center.chHello,
We are a team of software engineers and data scientists from the Swiss AI Center. Over the past two or three years, we have written a comprehensive guide for transitioning ML projects from experimentation to a real production setup.
We selected suitable tools to minimize friction within established workflows and teams, with a particular focus on SMEs by using simple, small components. Our goal is to provide a practical, step-by-step guide to MLOps.
This guide is entirely open source on GitHub and might be of interest to some of you. It is used both to teach students and to train companies in Western Switzerland.
Thanks! This is useful.
Is there a Podman version of this (versus the default Docker)? Would love to test it out with that...
cheers
Not quite. I personally like podman better than docker, but for the guide we went for the simplest or more mainstream tools, in order to keep it simple and to reduce the amount of testing needed for the guide.
Using podman shouldn't that big of a change though. BentoML that we present in the guide can also be used with podman instead of docker using the `--backend` flag, so you should be good to go.