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Answer: Use Vertex AI Pipelines to execute the experiments. Query the results stored in MetadataStore using the Vertex AI API.
The best approach for tracking and reporting experiments while minimizing manual effort is to use Vertex AI Pipelines. These pipelines help automate machine learning workflows, including data preparation, training, and evaluation. Vertex AI Pipelines integrate seamlessly with Vertex ML Metadata to automatically track experiment runs, metrics, and artifacts. This eliminates the need for manual data collection. The Vertex AI API allows querying the MetadataStore to retrieve the experiment details and accuracy metrics. This combination of tools provides a comprehensive solution for rapid experimentation without the overhead of manual tracking.
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Your data science team is working on multiple machine learning models and needs to conduct rapid experimentation with various features, model architectures, and hyperparameters. They need a system that can automatically track the accuracy metrics of these experiments over time and provide an API for easy querying and analysis of the results. What should they use to track and report their experiments while minimizing manual effort?
A
Use Vertex AI Pipelines to execute the experiments. Query the results stored in MetadataStore using the Vertex AI API.
B
Use Vertex AI Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
C
Use Vertex AI Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
D
Use Vertex AI Workbench user-managed notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.