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?
Click on the arrows to vote for the correct answer
A. B. C. D.C.
To design a customized deep neural network in Keras that predicts customer purchases based on their purchase history and explore model performance using multiple model architectures, store training data, and compare the evaluation metrics in the same dashboard, the best approach is to create an experiment in Kubeflow Pipelines to organize multiple runs.
Kubeflow Pipelines is an open-source platform for building and deploying portable, scalable machine learning workflows based on containers. It provides a visual interface for creating and managing machine learning pipelines, allowing users to define, execute, and monitor complex workflows. It integrates with popular ML tools and frameworks such as TensorFlow, Keras, PyTorch, and more.
Creating an experiment in Kubeflow Pipelines allows for organizing multiple runs, comparing models with different architectures, storing training data, and tracking evaluation metrics in the same dashboard. The following steps can be taken to create the experiment:
Define the problem and the objective: In this case, the objective is to predict customer purchases based on their purchase history using a deep neural network.
Prepare the data: The data should be preprocessed and split into training and validation sets.
Define the model architecture: Different model architectures can be designed and tested to find the one that performs best.
Define the hyperparameters: Hyperparameters such as learning rate, batch size, and number of epochs can be tuned to optimize model performance.
Create a pipeline in Kubeflow Pipelines: A pipeline can be created in Kubeflow Pipelines to organize multiple runs of the deep neural network with different architectures and hyperparameters. The pipeline can also include steps for data preprocessing and evaluation.
Run the pipeline: The pipeline can be run to train and evaluate the deep neural network with different architectures and hyperparameters. The results can be tracked and compared in the same dashboard.
Therefore, option D - Create an experiment in Kubeflow Pipelines to organize multiple runs is the correct answer for this question.