mlops-zoomcamp VS awesome-mlops

There is MLOps Zoomcamp course (which shows end-to-end MLOps process with open-source MLOps tools) https://github.com/DataTalksClub/mlops-zoomcamp.

Yes, your point exact. I haven't found any quality ones to be honest. There is the coursera mlops course (i did complete this one) which covers tfx (and other services like kubeflow but only scratching the surface) but that seems to be an overkill for me and also very dependant on tensorflow. I have came across this lately https://github.com/DataTalksClub/mlops-zoomcamp (havent went through it in detail) which seems to cover a lot but still missing both the full picture example and i dont see e.g. A/B testing, canary there

datatalksclub https://github.com/DataTalksClub/mlops-zoomcamp

DataTalksClub/mlops-zoomcamp: Free MLOps course from DataTalks.Club (github.com)

I found this one and also online events hosted by the MLOps community.

I think this course is a great one for practical MLOps. They have video lectures with homework https://github.com/DataTalksClub/mlops-zoomcamp

This is one of the possible resources online https://github.com/DataTalksClub/mlops-zoomcamp. It's a free course. And I also know that the MLOps community hosts many events around the topic.

I had seen a GitHub which uses like popular open source projects to build an end to end ml lifecycle. Im planning to follow it. https://github.com/DataTalksClub/mlops-zoomcamp

Maybe this will help https://github.com/DataTalksClub/mlops-zoomcamp

I'm in the same boat. Data scientist shifting towards ML engineering-MLOps. The guide seems quite complete. I am also doing the ML DevOps engineer, which has end-to-end projects and has been helpful so far. I also feel that most ML engineers will be Mlops too, as most companies will not distinguish between the two, so I try to focus on this part. Here is a quite comprehensive list of resources: https://github.com/visenger/awesome-mlops

Good Reference: https://github.com/visenger/awesome-mlops (The Link above has so many Guides, It's insane) https://madewithml.com/

I recently did a deep-dive into MLOps for a client, and I've found that https://ml-ops.org/ offers a great overview. Some topics are a bit too "zoomed out", but they still touch on most important aspects of building an end-to-end product. I found it a great starting point when doing research, and picking and choosing some key points from each section + some google helped a lot. Give it a look, you'll probably find some useful things in there.

MLOps

She made mentions to ML-Ops and MLFlow including Vertex AI the GCP implementation. I will post the video as soon as it is available. In the meantime, you can enjoy any other talk from Nerea Luis

From your post, you seem to be trained for data science for physics modeling, so I'd recommend to get started with https://ml-ops.org/ and for the data engineering part, I found this https://github.com/andkret/Cookbook open source cookbook to be invaluable.