s5cmd VS metaflow

s5cmd - Extended character support for s3 compatible backend

I've used a tool in the past called s5cmd to copy millions of objects, and it was strikingly fast: https://github.com/peak/s5cmd

Some folks say https://github.com/peak/s5cmd is faster than the two options above.

RUN mkdir /tmp/output && cd /tmp/output RUN wget --no-check-certificate https://github.com/peak/s5cmd/releases/download/v1.2.1/s5cmd_1.2.1_Linux-64bit.tar.gz RUN tar xvzf s5cmd_1.2.1_Linux-64bit.tar.gz && mv s5cmd /usr/bin/s5cmd && rm -rf /tmp/output && rm s5cmd_1.2.1_Linux-64bit.tar.gz

Not that I’ve seen but you might checkout https://github.com/peak/s5cmd

I like to use https://github.com/peak/s5cmd

Excellent walkthrough, love boto. We’ve recently been using s5cmd which we’ve found is ridiculously faster than boto without any extra boto tricks.

https://github.com/peak/s5cmd

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.