You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?
Creating an experiment in Kubeflow Pipelines is the best approach for organizing multiple runs, comparing evaluation metrics, and storing training data. Kubeflow Pipelines is specifically designed to manage and compare complex machine learning workflows, making it well-suited for exploring multiple model architectures and tracking their performance in a customized manner.
D - https://www.kubeflow.org/docs/about/use-cases/
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.
D. In the new Vertex AI, it now supports experimentation with hyper parameter tuning.
How can we track the progress of each run and compare the results in the vertex AI dashboard?
C anyone? D seems to me like an overkill.
(C) presents the most specific solution for what the question asks for: experimenting with models with their due comparisons. All of this is possible with the AI Platform. Furthermore, the question only speaks of experimentation. Kubeflow would be more powerfull if was a necessity for end-to-end pipeline.
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.
https://cloud.google.com/vertex-ai/docs/experiments/user-journey/uj-compare-models
D should be correct
I would vote for D but if C had said instead "different job names" .. would that have been a better option?
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.
https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/ https://www.kubeflow.org/docs/components/pipelines/concepts/run/
D option C does not work since CAIP have updated to VertexAI
With Kubeflow Pipelines, you can create experiments that help you keep track of multiple training runs with different model architectures and hyperparameters.
Went with D
C has similar job name, which make it wrong So correct answer should be D
D - everything else is just nonsense
D) Experiments is the way forward