Cookbook VS awesome-mlops

https://github.com/andkret/Cookbook arunca un ochi aici. Omul are si youtube channel https://www.youtube.com/@andreaskayy

I can recommend https://learndataengineering.com by Anreas Krenz. Will guide you via all important topics starting from sql & python to building pipelines using AWS/GCP. I used to participate for 1 year (costs ~ 200 Euro/220$). It's a self-paced. So for ~15h/week you can switch into DE position for appr. 6 months.

I wonder if anyone involved in this post and comments have tried this? https://learndataengineering.com/

I think it's allowed: https://learndataengineering.com

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.

I am doing this currently to fill in the blanks: https://learndataengineering.com. Also, do you know Python? If not take class on Udemy on that. Finally, data engineering is all about tools these days. I saw someone recommended this book here: Data Engineering with Python, I find it super hopeful. You download these tools (Apache Airflow, etc) and get a go with it. I am going to build some data pipelines via this book :)

the best way to be a dev or DE is to build stuff, not learning about algorithms. Just google DE academy, bootcamp or so. The linked one is quite good for a cheap price. A degree prepares you mostly for a PhD, not for a job. So dont look for degrees preparing you for a job in general.

I usually don't do self promotion, but because you directly asked for a good source. Look at my academy: https://learndataengineering.com

Find something like https://learndataengineering.com/, udemy or any other 'bootcamp/course' that goes on for few months and learn it. It is important that you will have some mentors or study buddies to exchange ideas or so.

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.