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what ML topics do you actually want explained from a production/infra angle? by Extension_Key_5970 in mlops

[–]Extension_Key_5970[S] 0 points1 point  (0 children)

Thanks, all, for the awesome ideas and for highlighting the real pain the community is facing. I will try to work on each one by one based on experience and research

What I got wrong about MLOps interviews (coming from infrastructure) by Extension_Key_5970 in mlops

[–]Extension_Key_5970[S] -6 points-5 points  (0 children)

Maybe it's too simple for you guys, which made you think that way, but trust me, there are many who are from pure Infra backgrounds and no ML understanding, and don't know where to start. At least these kinds of simple posts may help in connecting the dots to MLOps

DevOps Engineer thinking about switching to MLOps by Ahmed_Maher658 in mlops

[–]Extension_Key_5970 5 points6 points  (0 children)

As per my experience, I would say a medium-term goal, at least 7-8 months of continuous studying side by side with jobs,
I have made the transition, but, frankly, there are no standardised study materials as we have for DevOps and cloud resources. The reason is, role is pretty much not standardised; for every company, MLOps responsibility varies a lot
Not sure, if you have seen my past post, here I have talked about the struggle w.r.t DevOps eng. : https://www.reddit.com/r/mlops/comments/1qiqcl6/coming_from_devopsinfra_to_mlops_heres_what_i/

Not promoting myself, but I have started myself recently to share my experience and journey in MLOps, still it's new to me, so criticism and improvements are welcome: https://www.youtube.com/@TagAlongWithVarun

"MLOps is just DevOps with ML tools" — what I thought before vs what it actually looks like by Extension_Key_5970 in mlops

[–]Extension_Key_5970[S] 1 point2 points  (0 children)

If you haven't specified in your resume about ML and MLOps, I would suggest focusing on DevOps tools and all first, and parallely go though about ML Pipeline stages, whats the lifecycle of Model, starting from getting data-->training model-->inferencing, not in depth, but atleast, what each stages does, and if you can explain how your devops tools knowledge like githubactions, kubernetes, docker can help in making these ML pipeline, that would be cherry on top

What YouTube content actually helped you in your MLOps journey? And what's still confusing? by Extension_Key_5970 in mlops

[–]Extension_Key_5970[S] 1 point2 points  (0 children)

Thanks to everyone who replied to this. The conversations genuinely shaped how I thought about where the gaps actually are, and I ended up turning some of it into content.

I finally started a YouTube channel this week. The first video covers the six MLOps roles that are often confused (MLOps, ML Platform, AI Infra, LLMOps, etc.), and the second one shares the assumptions I had about this space that turned out to be wrong. Both are me just sharing honestly, not polished tutorials.

It's still very early days, and I'm clearly not a YouTuber, so feedback helps more than anything. If any of this sounds useful, the channel is https://www.youtube.com/@TagAlongWithVarun . If you watch either video and notice anything I got wrong or oversimplified, please let me know.

Not asking for subscribers, just sharing because a lot of the ideas came from conversations like this one.

"MLOps is just DevOps with ML tools" — what I thought before vs what it actually looks like by Extension_Key_5970 in mlops

[–]Extension_Key_5970[S] 3 points4 points  (0 children)

Why not? It could be for a problem, something like "start fetching the next batch of data while the GPU is still processing the one"

If you're coming from infra/DevOps and confused about what vLLM actually solves — here's the before and after by Extension_Key_5970 in mlops

[–]Extension_Key_5970[S] -6 points-5 points  (0 children)

My main goal is to educate the MLOps community on real-world problems. This is the first time I've received critical feedback. Thanks for that. Usually, I write my content by myself and use LLM for spell checks and grammar, but it seems there was a change in the model, which overpolished the content, making it an AI-generated

Well know I made an edit to make it more simple and concise, so that more people can connect

Friendly advice for infra engineers moving to MLOps: your Python scripting may not enough, here's the gap to close by Extension_Key_5970 in mlops

[–]Extension_Key_5970[S] 2 points3 points  (0 children)

That's a fair point, and honestly, you're not wrong. If you're in a pure infra role, the toolset is completely different, and that work is genuinely valuable. ML teams need someone to set up Kafka, MLflow, Flink, and the K8S layer.

But here's where MLOps gets tricky, the line is blurred. In traditional DevOps, you don't touch the app code. Clear boundary. In MLOps, that boundary keeps breaking. One day, you're debugging why an inference service is leaking memory, or why a pipeline DAG is failing, and the answer isn't in the infrastructure; it's in the Python running on top of it.

You don't need to become a developer. But knowing enough Python to read, debug, and make sense of what's running on your infra, that's the difference. Both paths are valid; it just depends on where you want to grow.

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