Machine Learning in Production (CMU Course)

The material is definitely practical—Kafka, Docker, Kubernetes, and Jenkins are all industry-standard tools, and the focus on MLOps is refreshing. It’s great to see a course bridge the gap between ML and actual production systems, not just stop at building models. Love that they&#x27;re also tackling explainability, fairness, and monitoring. These are the things that often get overlooked in practice.<p>Is it too entry-level? Looking at the labs, a lot of this seems like stuff a mid-level software engineer (or even a motivated beginner) could pick up on their own with tutorials. Git, Flask, container orchestration... all useful, but pretty basic for anyone who&#x27;s already worked in production environments. The deeper challenges—like optimizing networking for distributed training or managing inference at scale—don’t seem to get as much attention. Maybe it comes up in the group projects?<p>Also wondering about the long-term relevance of some of the tools they’re using. Jenkins? Sure, it’s everywhere, but wouldn’t it make sense to introduce something more modern like GitHub Actions or ArgoCD for CI&#x2F;CD? Same with Kubernetes—obviously a must-know, but what about alternatives or supplementary tools for edge deployments or serverless systems? Feels like an opportunity to push into the future a bit more.

Fascinating; I just looked through the labs, and as a fullstack developer without that much experience in LLMs, it looks like I&#x27;m already closely familiar with half of them (git, flask, kafka, kubernetes) and the other half is just... code. No crazy math that I&#x27;ve come to associate with ML.<p>Does it mean that ML ops is a field that&#x27;s actually not that hard to approach for a regular developer without a PhD?

This seems to have very little on Data Quality and it is on Chapter 16...How much practical experience in Industry do the authors have? Because 90% of your time will be spent on Data Quality and Data Cleansing...

Is there somewhere I could follow along with other non students?

Can anyone sign up or do we have to get accepted into one of the top computer science programs in the country?

Does anyone know about literature or courses regarding building machine learning cluster infrastructure? I am mainly interested in building and scaling up the storage infrastructure, networking and scheduling approaches.

Great to see this course here. Christian is also great as a person and he makes great work. I know some of the beginnings of this course and book and can highly recommend it.

I&#x27;ve worked on ML platforms and systems for 9.5 years at every scale. The material looks great.

Maybe I am underestimating the course complexity but this sounds like an entry level course. Up until Model explanability tools, most of the stuff looks fairly straightforward tbh. Although, they&#x27;re using industry standard tools for most use-cases which is good I think.

I like the idea of learning a single “Kubernetis”