I really like ClearML

😄 Yes

right?

What's that mean? can you give me an example

I feel ClearML is an end-to-end MLOps platform

I am not very familiar with KubeFlow but as far as I know it is mainly for orchestration whereas ClearML offers a full E2E solution 🙂

from data versioning, model serving, model experiment

clearml-agent is for orchestration - remote execution. clearml is the python package you need to install and add the magic lines of code:
https://github.com/allegroai/clearml

I really don't understand about orchestration

can you explain simpler?

I see two versions: clearml and clearml-agent

Thank you

but

I don't really understand about remote execution

It can run dockers and it can run over K8s

I want to build end2end mlops platform, so i just use only clear-agent natanM?

It means you can run your code on a different machine very very easily using ClearML 🙂

like docker or K8s?

That means I can use ClearML Agent instead of Kubernetes, right?

It depends on what you use K8s for

Thank you

but you can use it with or without K8s

run on dockers - i.e. execute your code inside of a docker image

I am thinking that ClearML Agent like K8s?

And orchestration 🙂

Sorry

I'm really confused when choosing a framework to build our end2end MLOps Platform.

Can you give me some advices?

It's a way to execute tasks remotely and even automate the entire process of data pre processing -> training -> output model 🙂
You can read more here:
https://github.com/allegroai/clearml-agent

I would advise using ClearML 😄