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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

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're also tackling explainability, fairness, and monitoring. These are the things that often get overlooked in practice. 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'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? 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/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.

amelius 2y ago
> Also wondering about the long-term relevance of some of the tools they’re using. That's what I was wondering about too. It seems to me that eventually someone will build a tool that runs any neural network on any hardware, whether local on one machine, or distributed in the cloud.
underdeserver 2y ago
Too entry level? Even if every tool is entry level, tying them all together and actually making it work is hard. I'd say it's mid-to-late B.Sc. material. Relevance? Is there really a huge conceptual difference between Jenkins and the other CI/CD frameworks? If not, if I were them I would just choose a random popular one, and it seems to me that's just what they did.
- ggddv 2y ago
  It’s kind of funny all those supposedly complicated technologies are actually pretty simple when you understand why you are using them. Docker is the best example, it’s hard to understand what is happening unless to understand the problem it’s solving.
kkukshtel 2y ago
I think what you're missing here is that this is now _the_ entry point for year 1 CS students. People come in wanting to do ML. 20 years ago people came in and learned to write databases with Java and used similarly "will probably be deprecated tools". This is just the new starting point.

amelius 2y ago

> Also wondering about the long-term relevance of some of the tools they’re using. That's what I was wondering about too. It seems to me that eventually someone will build a tool that runs any neural network on any hardware, whether local on one machine, or distributed in the cloud.

underdeserver 2y ago

Too entry level? Even if every tool is entry level, tying them all together and actually making it work is hard. I'd say it's mid-to-late B.Sc. material. Relevance? Is there really a huge conceptual difference between Jenkins and the other CI/CD frameworks? If not, if I were them I would just choose a random popular one, and it seems to me that's just what they did.

ggddv 2y ago
It’s kind of funny all those supposedly complicated technologies are actually pretty simple when you understand why you are using them. Docker is the best example, it’s hard to understand what is happening unless to understand the problem it’s solving.

kkukshtel 2y ago

I think what you're missing here is that this is now _the_ entry point for year 1 CS students. People come in wanting to do ML. 20 years ago people came in and learned to write databases with Java and used similarly "will probably be deprecated tools". This is just the new starting point.