metaflow VS Hystrix

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.

Oops, forgot to include the Hystrix link, https://github.com/Netflix/Hystrix

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).

When breaking up a monolithic app into microservices, the communication between these services becomes vital to the health and performance of the application. Technically, you could incorporate the features to manage this traffic directly into your application. This is what Twitter, Google, and Netflix did with massive internal libraries like Finagle, Stubby, and Hysterix.

Hystrix: https://github.com/Netflix/Hystrix Hollow: https://hollow.how/

Have you actually read the article and maybe also https://github.com/Netflix/Hystrix status section??!

I came upon Resilience4J when I was running my talk on the Circuit Breaker pattern. The talk included a demo, and it relied on Hystrix. One day, I wanted to update the demo to the latest Hystrix version and noticed that maintainers had deprecated it in favor of Resilience4J.

Netflix was talking alot about circuit breaks a few years ago, and had the Hystrix project. Looks like Hystrix is discontinued, so I'm not sure if there are good library solutions that are easy to adopt. Overall I don't see it getting talked about that frequently... beyond just exponential backoff inside a retry loop.

- https://github.com/Netflix/Hystrix