What should they use to track and report their experiments while minimizing manual effort?

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time.

What should they use to track and report their experiments while minimizing manual effort?
A . Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
B . Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
C . Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
D . Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API

Answer: A

Explanation:

https://codelabs.developers.google.com/codelabs/cloud-kubeflow-pipelines-gis Kubeflow Pipelines (KFP) helps solve these issues by providing a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. Cloud AI Pipelines makes it easy to set up a KFP installation.

https://www.kubeflow.org/docs/components/pipelines/introduction/#what-is-kubeflow-pipelines

"Kubeflow Pipelines supports the export of scalar metrics. You can write a list of metrics to a local file to describe the performance of the model. The pipeline agent uploads the local file as your run-time metrics. You can view the uploaded metrics as a visualization in the Runs page for a particular experiment in the Kubeflow Pipelines UI."

https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/