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

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

I would advise using ClearML 😄

Can you give me some advices?

I really like ClearML

😄 Yes

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 really don't understand about orchestration

I see two versions: clearml and clearml-agent

And orchestration 🙂

can you explain simpler?

right?

Sorry

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

I don't really understand about remote execution

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

Thank you

but

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

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

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

I am thinking that ClearML Agent like K8s?

It can run dockers and it can run over K8s

It depends on what you use K8s for

like docker or K8s?

but you can use it with or without K8s

Thank you