Chansung Park

@osanseviero is there any updated news?

@osanseviero no problem. please let me know when anything is decided. Cheers! :)

@ConverJens Hi. First of all, thank you! I don't think it is the problem on model's side. I get the correct shape of Tensor from the prediction, but the problem...

sure, I mean I have no problem to run `Trainer` and `Tuner` components with `train` and `eval` splits from `Trasnform` component. But why do I have a problem with `Evaluator`...

yeap, actually I tried the similar approach too like below: ```python ## Serving signature in Trainer def _transformed_name(key: str) -> str: return key + "_xf" def _get_signature(model): signatures = {...

what does label data to the `Evaluator` has anything to do with the outputs from the model? model doesn't know the label data, but Evaluator does. Serving spec is no...

Connection might not be stable. Have a look at the log (output of the running cell). It should spit something out when it receives something. Otherwise please refresh the session

@peterschmidt85 addressed your comment. please take a look one more time! Thanks!

@peterschmidt85 all addressed!

@doender Vertex AI pipeline can be triggered in any ways, and you can benefit many things by triggering it in Cloud Function. For example: - Cloud Function can be triggered...