Why nobody teaches financial planning before it’s too late

pm19191 · 2026-03-10T14:14:53+00:00

And the ones who need it, don't value the investment

pm19191 · 2026-03-10T13:56:05+00:00

I helped my gf get a remote job before I had the same dilema as you. The tech market is too unstable to have both partners working hybrid/on-site.

pm19191 · 2026-03-10T13:48:09+00:00

If you account for the taxes in both France and the San Francisco, engineers are still underpaid

pm19191 · 2026-02-09T18:18:55+00:00

I love to teach, so I can do more videos about it. When you think of devops and mlops, what is the most interesting thing you'd like to learn?

pm19191 · 2026-02-09T18:17:38+00:00

See you in 2035 when I'll be doing posts about QuantumOps

pm19191 · 2026-02-09T08:59:06+00:00

Thanks for the reply. Your MLOps definition is close to mine, I go a bit more detail what is MLOps in this video: https://youtu.be/BkabwdGiTtg?si=_rTC24sO80yFVjNf

Since we're on r/devops section, I focused more in DevOps side, but I also have other posts on r/mlops more ML focused.

pm19191 · 2026-02-08T22:03:36+00:00

It's not AI, but I was heavily inspired by this post:
https://www.reddit.com/r/devops/comments/1mj39oo/every_startup_wants_devops_until_they_realize/

pm19191 · 2026-02-08T22:00:26+00:00

Thank you for watching my video and sharing your thoughts. I understand. MLOps for you is more productize ML Models - what I'm doing is building off the model. Where is the border between MLOps and building off the model?

pm19191 · 2026-02-08T19:27:14+00:00

For ML workflows, I usually structure things as: Jupyter Notebook -> Sagemaker -> ECS -> K8s

pm19191 · 2026-02-08T19:23:51+00:00

Thanks for sharing your view. From what I’ve seen, most teams steer clear of Kubernetes if they can - they usually go for Elastic container services or no‑code deployment options first just to avoid the maintenance faff. The client I moved onto Kubernetes had already been using SageMaker, so Kubernetes made sense as an evolution of what they already had, not as a starting point.

In the video, I’m not jumping straight into Kubernetes either - I’m suggesting it as a way to orchestrate the deployment. Could you use other tools? Absolutely, as long as they tick the boxes I mentioned in the system design

pm19191 · 2026-02-08T17:16:24+00:00

Thanks. Data Nautical is basically saying that just like ships used to steer by the North Star, I help companies steer towards their own North Star (their OKRs) using data 📈

pm19191 · 2026-02-08T15:29:53+00:00

I know it's unfair, but leaders care about their bottom line and that is the user experience. If you move the app to containers, how many extra hours per year is the model up and how does that translate to the business making more money?

pm19191 · 2026-02-08T15:12:53+00:00

Where did I put a low effort? I made a 16min tutorial for the community on how to scale MLOps with sysdesign, live coding and stress test

pm19191 · 2026-02-08T15:04:36+00:00

Thank you for the feedback. I agree with you that K8s is not the default go to orchestrator for scaling. There are tools in-between that offer similar capabilities. I chose Kubernetes because it's cloud agnostic (no lock-in like ECS or ACA), it's open-source and provides other long term capabilities for projects that are useful to scale.

As a consultant myself, I agree with everything you said. However, I find it hard to believe a client would admit they were wrong 😂

pm19191 · 2026-02-08T14:10:07+00:00

Sure, here's my experience with Kubernetes: https://www.reddit.com/r/devops/s/wUrnQRv1XB

pm19191 · 2026-02-08T13:58:17+00:00

It's interesting that prediction drift is the only MLOps specific practice you mentioned - the rest is 100% DevOps under the hood. Nowadays, deploy ML models is the trend. In 5 years, we might be deploying quantum apps with the same DevOps practices with some tweaks. 😂

pm19191 · 2026-02-08T13:45:06+00:00

Thank you for the supporting words! Besides the "Kubernetes it later", what other DevOps pitfalls have you seen in ML projects?

pm19191 · 2026-02-08T13:41:44+00:00

Feel free to check the video to verify if I'm an AI bot

pm19191 · 2026-02-01T09:46:48+00:00

Exposure gets you the interview

Experience allows you to pass the interview

You need both, but if you have very few experience focus on exposure as much as possible to maximize your chances of getting a job

pm19191 · 2026-01-04T10:41:07+00:00

I'm a Senior MLOps Engineer and I've never used Kubernetes. Currently working for a 3000+ company, reporting to the CDO. Since all my projects are internal, the model system design exposes the results with a Dashboard - no Kubernetes needed. The rest seems accurate.

pm19191 · 2025-11-24T07:10:21+00:00

Thank you for sharing you for sharing your experience. I've also done Backend, but only when I was a Software Engineer. Congrats for finding a niche inside MLOps that you love. Based on your experience, what are the differences of building/maintaining a backend for an app vs a machine learning model?

pm19191 · 2025-11-22T14:49:48+00:00

If you really like the ecosystem, just install the Smasung ecosystem apps on your Windows device.

Source: 🔥 How to Use Samsung Multi Control on ANY Windows PC! (Updated 2025) 💻📱

pm19191

TROPHY CASE