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I've worked on ML platforms and systems for 9.5 years at every scale. The material looks great.
What would you recommend for a backend developer looking to make the switch from rest crud apps to ml platforms?
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.
You missed model serving (I think ?). Tough and latency sensitive esp for recommender systems. Prone to latency spikes, traffic spikes. Even with a well-written Python code you can run into limitations quite quickly.
Fascinating; I just looked through the labs, and as a fullstack developer without that much experience in LLMs, it looks like I'm already closely familiar with half of them (git, flask, kafka, kubernetes) and the other half is just... code. No crazy math that I've come to associate with ML. Does it mean that ML ops is a field that's actually not that hard to approach for a regular developer without a PhD?
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.
Nothing fancy. The core principles are the same, you'll need to adapt them depending upon the kind workload changes that ML introduces. For most ML systems 1. Storage infra: assuming storage for models or even data, use any blob storage like S3. Or a shared networked file system like EFS, Lustre etc. 2. Networking: if you're talking about networking using large GPUs, I am not aware of any definitive resource on this.…
> Nothing fancy.
Well, right now I am seeing lots of low-level innovation for networking/storage along with RoCE, Infiniband, Tesla's ttpoe, the recent addition of devmem-tcp to the linux kernel (https://docs.kernel.org/networking/devmem.html) and wondered if there are approaches on how to plug something like that together on a higher level and what the considerations are. I surely assume EFS or S3 might be too expensive for a (large) training infrastructure, but I can be wrong?
> You missed model serving (I think ?).
I think I have a better grasp on the engineering challenges there and could imagine an architecture to scale that out (I believe!).
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.
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…
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.
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…
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.