A Simple MLOps Pipeline on Your Local Machine

A Simple MLOps Pipeline on Your Local Machine

In this tutorial, i wil show you how to create A Simple MLOps Pipeline on Your Local Machine. Docker Desktop, Kubernetes and Other Open Source Tooling to Demonstrate Machine Learning Process on your Laptop

It can be difficult to get any kind of ops experience outside of enterprise without turning to the cloud. The simple reason is that there’s little benefit in not turning to the cloud. In order to provide a zero-cost entry point into MLOps for those new to the space, I wanted to see if I could set up a simple MLOps pipeline in a reasonable amount of time on my personal laptop.

This is a “tip of the iceberg” tutorial for MLOps. Much of it will be modeled loosely after the famous Continuous Delivery for Machine Learning paper (Sato et al, 2019) — but with much less depth considering I’m just one dude putting in a few hours here and there. Instead of a real “pipeline”…think of it more like Michael Scott’s Tube City.

Some of technologies used will be:

  • Python
  • Docker/Kubernetes
  • Mlflow
  • Minio
  • Seldon Core
  • Github
  • Jenkins
  • ArgoCD

None of this is fit for production as outlined, but many of the principles and technologies are widely applicable. You’ll really only need Docker Desktop to follow along. This will enable you to run Kubernetes. However, you should also clone this repo, as it has all of the additional scripts we’ll need.


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