MLflow 101

MLflow Part 2: Deploying a Tracking Server to Minikube!

Creating a point for logging and tracking model artifacts in a single server running on Minikube

Towards Data Science
10 min readOct 8, 2020

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10/15/20 Update: In writing my next post in this series, I found several bugs that prevented me from appropriately deploying to Minikube. To that end, I’ve updated a number of things to get you up and going with a WORKING instance! 😃

Welcome back, friends! We’re back with our continued mini-series on MLflow. In case you missed out part one, be sure to check it out here. The first post was a super basic introduction to log basic parameters, metrics, and artifacts with MLflow. That was just having us log those items to a spot on our local machine, which is not an ideal practice. In a company context, you ideally want to have all those things logged to a central, reusable location. That’s we’ll be tackling in today’s post! And of course, you can find all my code on GitHub at this link.

So to be clear, we’re going to be covering some advanced topics that require a bit of foreknowledge about Docker and Kubernetes. I personally plan to write posts on those at a later date, but for now, I’d recommend the following resources if you want to get a quick start on working with Docker and Kubernetes:

Now if you know Kubernetes, chances are that you are familiar with Minikube, but in case you aren’t, Minikube is basically a small VM you can run on your local machine to start a sandbox environment to test out Kubernetes concepts. Once Minikube is up and running, it’ll look very familiar to those of you who have worked in legit Kubernetes environments. The instructions to set up Minikube are nicely documented in this page, BUT in order to get Minikube working, we need to get a couple additional things added later on down this post.

Before going further, I think a picture is worth a thousand words, so below is a tiny picture of the architecture we’ll be building here.

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Principal machine learning engineer at a Fortune 50 company, 5x AWS certified, 2x HashiCorp certified, 1x GCP certified, M.A. in Org Leadership, PMP, ChFC, CSM