When users starts Serve cluster with a set of options (http options, checkpoint path) and then connects to it with a different set of options, we should either update it, or error out.

Apart from Unit and Integration tests, we should also run the st2-self-check script for every PR as part of the CI pipeline.
Something we do in e2e and manual testing.

It should be possible with the Github Actions and would help us to catch bugs before merging into master.
Ex #5489.

Is your feature request related to a problem?

No.

Provide a detailed description of the proposed feature

After replacing most of the generic CX exceptions with #2643, I saw that there are still some generic CX exceptions which could not be replaced with Python built-ins. To replace those with more meaningful ones we should extend the CobblerException class to include more exceptio

Is your feature request related to a problem? Please describe.
It's cumbersome to create the same step twice.

Describe the solution you'd like
Add a button to duplicate a step in the pipeline editor.

Ideas
We could combine this with some other ideas in a context menu (right click).

Credit to Serhii Ostapchuk for contributing this on Slack.

Hi.
A question or feature request... TBH I have no idea how to set this up currently.
I have a config for two pipelines and the second needs to be started only if the first one finishes
In the current setup when I upload the file to
bucket_name: bucket.name
object_key: key_2/package.zip
the second pipeline starts.
But how to configure it in a way that second will run

🚀 The feature

In version 0.5.x we have full support for YOLOv5, at the cost of introducing more dependencies. If someone just wants to use the models for inference, he/she doesn't need all additional features like:

• visualizations
• profiling
• type conversions to pandas
• ...

However, those useful features are (pa

Given the new key-value store event stream, it'd be nice to have something like:

$ skein kv events <application id> [options...]

where the process blocks, and logs the event stream to the console until interrupted. This would be useful for debugging, as well as demos.

Description

There is no clear documentation about how one should approach deployment once using kedro-mlflow (especially with custom models). I would love to see how one integrates CI/CD or other Ops style tools to achieve code -> mlflow model -> deployed model workflows (as many issues are faced trying to use mlflow serve).

Context

Having a clear cut understanding of the deployment p