Exam Professional Machine Learning Engineer topic 1 question 34 discussion

D - https://www.kubeflow.org/docs/about/use-cases/

Should be D

D) Experiments is the way forward

I would vote for D but if C had said instead "different job names" .. would that have been a better option?

D - everything else is just nonsense

D should be correct

C has similar job name, which make it wrong So correct answer should be D

The best approach is to create an experiment in Kubeflow Pipelines to organize multiple runs. Option A is incorrect because AutoML Tables is a managed machine learning service that automates the process of building machine learning models from tabular data. It does not provide the flexibility to customize the model architecture or explore multiple model architectures. Option B is incorrect because Cloud Composer is a managed workflow orchestration service that can be used to automate machine learning workflows. However, it does not provide the same level of flexibility or scalability as Kubeflow Pipelines. Option C is incorrect because running multiple training jobs on AI Platform with similar job names will not allow you to easily organize and compare the results.

Went with D

With Kubeflow Pipelines, you can create experiments that help you keep track of multiple training runs with different model architectures and hyperparameters.

https://cloud.google.com/vertex-ai/docs/experiments/user-journey/uj-compare-models

D option C does not work since CAIP have updated to VertexAI

https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/ https://www.kubeflow.org/docs/components/pipelines/concepts/run/

D- we need to use experiments feature to comapre models,having different jobnames is not going to help track experiments.

C for me. It only talks about experimentation .. thats where AI platform fits better.

Similar job names is a bit of a confusion creator as we can not use same job names for sure. D sounds better but better in vertex AI during experiment phase only.

C anyone? D seems to me like an overkill.