metaflow VS vitess

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.

With Vitess likely merging a lot of its binaries into a single unified binary: https://github.com/vitessio/vitess/issues/7471#issuecomment-...

... it would be a wild future if Vitess replaced the underlying MySQL engine with this as long as the performance is good enough.

Thank you for the compliment!

We recently started adding support for CTEs in Vitess! You can check out https://github.com/vitessio/vitess/pull/14321 if you want to see some technical details of the implementation.

For now, we have added preliminary support by converting them to derived tables internally, but we believe that we need to make CTEs first-class citizens themselves of query planning. Once we make that change, we can look towards supporting recursive CTEs.

This however will take some time, but then, all good things do!

Why would it be a Google project? https://github.com/vitessio/vitess

This is great news. I strolled around https://github.com/vitessio/vitess/issues/12967.

Are there any public discussions of more trade-offs vitess has to make to enable fks?

MySQL + Vitess I noticed Slack gets by using MySQL because they're using Vites. From Slack's post (https://slack.engineering/scaling-datastores-at-slack-with-vitess/) it seems like they choose Vites because it facilitated a smooth transition because it's built on top of MySQL.

This sounds very similar to the connection pooling done by vitess https://vitess.io/.

https://github.com/vitessio/vitess/issues/12967

A lot of this is possible thanks to the magic of Vitess.

"Vitess is a database clustering system for horizontal scaling of MySQL."

https://github.com/vitessio/vitess