awesome-mlops VS metaflow

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.

I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home

1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling.

even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.

They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural networks to downscale video.. Sound familiar? That’s cause that’s practically the same thing as Nvidia’s DLSS (which upscales instead of downscales).

Check out Metaflow by Netflix

Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project.

It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.