aws-cli VS metaflow

The AWS CLI is a must-have tool if your team relies on Amazon Web Services. It lets you effortlessly interact with AWS services, orchestrate resource management, and automate tasks from the comfort of your terminal. Once you get used to the tool, you'll notice how convenient and quick it is to fit into your processes – especially compared to going through AWS's web-based user interface.

Just for your consideration, the network effect is very real with package managers, too:

https://search.nixos.org/packages?channel=23.05&show=awscli2 is 2.11.27 (even on the "unstable" channel), versus https://formulae.brew.sh/formula/awscli#default that is 2.12.1, which correctly is the most current (https://github.com/aws/aws-cli/tags)

s3fs allows Linux, macOS, and FreeBSD to mount an S3 bucket via FUSE(Filesystem in Userspace). s3fs makes you operate files and directories in S3 bucket like a local file system. s3fs preserves the native object format for files, allowing use of other tools like AWS CLI.

AFAIK it's still a Python package: https://github.com/aws/aws-cli/tree/2.11.6

Check CLI version, it looks like the earliest version that had DEEP_ARCHIVE support was 1.16.133 (https://github.com/aws/aws-cli/commit/55efab102f774ef17bf51ad32e939e4e03a5ff8a)

And this is the init_localstack.sh file content, a unique thing about localstack its that you can move all strings like an aws-cli tool, also the container deletes all the content and config once the container stops, so the script file must create all the resources that you need from Localstack

It can? I created a pull request for this that hasn't been merged yet: https://github.com/aws/aws-cli/pull/6365

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.