You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to…

Kubeflow Pipelines is a service that allows you to create and run machine learning workflows on

Google Cloud using various features, model architectures, and hyperparameters. You can use

Kubeflow Pipelines to scale up your workflows, leverage distributed training, and access specialized

hardware such as GPUs and TPUs1. An experiment in Kubeflow Pipelines is a workspace where you

can try different configurations of your pipelines and organize your runs into logical groups. You can

use experiments to compare the performance of different models and track the evaluation metrics in

the same dashboard2.

For the use case of designing a customized deep neural network in Keras that will predict customer

purchases based on their purchase history, the best option is to create an experiment in Kubeflow

Pipelines to organize multiple runs. This option allows you to explore model performance using

multiple model architectures, store training data, and compare the evaluation metrics in the same

dashboard. You can use Keras to build and train your deep neural network models, and then package

them as pipeline components that can be reused and combined with other components. You can also

use Kubeflow Pipelines SDK to define and submit your pipelines programmatically, and use Kubeflow

Pipelines UI to monitor and manage your experiments. Therefore, creating an experiment in

Kubeflow Pipelines to organize multiple runs is the best option for this use case.

Reference:

Kubeflow Pipelines documentation

Experiment | Kubeflow