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Answer: Use Kubeflow Pipelines to execute the experiments. Export the metrics file, and query the results using the Kubeflow Pipelines API.
The correct answer is A: 'Use Kubeflow Pipelines to execute the experiments. Export the metrics file, and query the results using the Kubeflow Pipelines API.' Kubeflow Pipelines is designed to help data science teams deploy robust, repeatable machine learning pipelines, and it includes features for monitoring, auditing, version tracking, and reproducibility. Using Kubeflow Pipelines minimizes manual effort by providing built-in support for experiment tracking and querying metrics through its API, making it a highly suitable choice for this task.
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Your data science team is working on several machine learning experiments and needs to rapidly iterate with various features, model architectures, and hyperparameters. Tracking and reporting the accuracy metrics of these experiments efficiently is crucial. They also need an API to query these metrics over time to analyze the performance of different configurations. What should they use to ensure smooth tracking and reporting of 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 AI Platform Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
C
Use AI Platform Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
D
Use AI Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.
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