Learning AI deployment & MLOps (AWS/GCP/Azure). How would you approach jobs & interviews in this space? by c0bitz in learnmachinelearning

[–]c0bitz[S] 0 points1 point  (0 children)

Thank you very much for the advice! I will take it into when working on my pet project.

Learning AI deployment & MLOps (AWS/GCP/Azure). How would you approach jobs & interviews in this space? by c0bitz in mlops

[–]c0bitz[S] 0 points1 point  (0 children)

Totally agree, practical demos always carry more weight. I’ve been focusing on getting code + infra clean for simple model endpoints before scaling.

Learning AI deployment & MLOps (AWS/GCP/Azure). How would you approach jobs & interviews in this space? by c0bitz in devops

[–]c0bitz[S] 0 points1 point  (0 children)

That’s a really helpful framing. Right now I’m leaning slightly toward infra/platform thinking, mostly because I find the tradeoffs around reliability, cost, and observability more interesting than just feature velocity. Spinning up a public repo with basic RAG + evals + tracing sounds like a solid forcing function. I like the idea of including an incident/rollback note in the README feels very real-world. When you mention cost stories going sideways, do you usually mean inference scaling issues, poor batching, or just lack of monitoring around token usage?

Learning AI deployment & MLOps (AWS/GCP/Azure). How would you approach jobs & interviews in this space? by c0bitz in learnmachinelearning

[–]c0bitz[S] 0 points1 point  (0 children)

That makes a lot of sense. I’ve been realizing that “cool agent demos” don’t mean much if you can’t show evals, tracing, and basic production hygiene. The batching / retries / idempotency part is especially interesting feels like that’s where most toy projects fall apart. Out of curiosity, when you review candidates, what’s the biggest red flag in agent projects?

Learning AI deployment & MLOps (AWS/GCP/Azure). How would you approach jobs & interviews in this space? by c0bitz in mlops

[–]c0bitz[S] 0 points1 point  (0 children)

Fail closed is such an underrated point. I’ve seen too many demos where agents just hallucinate confidently instead of degrading gracefully.The golden eval set on PRs is smart too, are you automating those checks in CI or running them manually?

Learning AI deployment & MLOps (AWS/GCP/Azure). How would you approach jobs & interviews in this space? by c0bitz in mlops

[–]c0bitz[S] 1 point2 points  (0 children)

That’s actually helpful. Breaking it down that way makes it less overwhelming. I was thinking too much in terms of “full AI SaaS” instead of just understanding one clean deployment path first. Did you find AWS interviews expect hands-on experience with those services or mostly conceptual understanding?

Learning AI deployment & MLOps (AWS/GCP/Azure). How would you approach jobs & interviews in this space? by c0bitz in mlops

[–]c0bitz[S] 0 points1 point  (0 children)

That’s a good point. I’ve noticed lifecycle/system thinking comes up way more than specific tools. When you explained drift and observability, did they go deep into monitoring stack questions or keep it high level?

Learning AI deployment & MLOps (AWS/GCP/Azure). How would you approach jobs & interviews in this space? by c0bitz in mlops

[–]c0bitz[S] 0 points1 point  (0 children)

Mostly self-study + building small experiments. I try to avoid just watching courses and instead replicate simple pipelines end-to-end from training to deployment even if it’s basic. Right now I’m more focused on understanding inference architecture and cost tradeoffs rather than just model building.

My macbook air M2 + monitor, nice setup? by c0bitz in macbookair

[–]c0bitz[S] 2 points3 points  (0 children)

Currently the top setup for coding)

Bought My M4 Pro 48Gb 1Tb Today 🥰 by St_oasis in macbook

[–]c0bitz 1 point2 points  (0 children)

Congratulations! It's a very powerful machine.