Member-only story
Hands-on Tutorials
Get started with MLOps
A comprehensive MLOps tutorial with open source tools
Getting machine learning (ML) models into production is hard work. Depending on the level of ambition, it can be surprisingly hard, actually. In this post, I’ll go over my personal thoughts (with implementation examples) on principles suitable for the journey of putting ML models into production within a regulated industry; i.e., when everything needs to be auditable, compliant, and in control — a situation where a hacked together API deployed on an EC2 instance is not going to cut it.
Machine Learning Operations (MLOps) refers to an approach where a combination of DevOps and software engineering is leveraged in a manner that enables deploying and maintaining ML models in production reliably and efficiently. Plenty of information can be found online discussing the conceptual ins and outs of MLOps, so instead, this article will focus on being pragmatic with a lot of hands-on code, etc., basically setting up a proof of concept MLOps framework based on open-source tools. You can find the final code on GitHub.
It is all about getting ML models into production; but what does that mean? For this post, I will consider the following list of concepts, which I think should be considered as part of an MLOps framework:
