MLOps guide

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2024-09-24 17:00:05

A collection of materials from introductory to advanced. This is roughly the path I’d follow if I were to start my MLOps journey again.

Table of contents ML + engineering fundamentals MLOps …. Overview …. Intermediate …. Advanced Career Case studies Bonus

While it’s tempting to want to get straight to ChatGPT, it’s important to have a good grasp of machine learning, deep learning, NLP, and reinforcement learning fundamentals.

Ops in MLOps comes from DevOps, short for Developments and Operations. To operationalize something means to bring it into production, which includes deploying, monitoring, and maintaining it.

Currently, this section contains a lot of my writing, certainly because of my bias and because when I set out to learn about MLOps, there wasn’t a lot of resources about it yet. I’ll add more materials soon!

In this detailed and well-written blog post, Chang described how Airbnb used machine learning to predict an important business metric: the value of homes on Airbnb. It walks you through the entire workflow: feature engineering, model selection, prototyping, moving prototypes to production. It’s completed with lessons learned, tools used, and code snippets too.