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A data science team is working on a project that requires testing various features, model structures, and hyperparameters efficiently. The team needs a solution that not only allows for rapid experimentation but also ensures accurate tracking and reporting of experiment metrics with minimal manual effort. The solution should support scalability, cost-effectiveness, and compliance with data governance policies. Given these requirements, which of the following approaches is the BEST for the team to adopt? (Choose two correct options)
A
Utilize AI Platform Notebooks for running experiments and manually record the results in a shared Google Sheets file, then use the Google Sheets API for querying the results.
B
Deploy AI Platform Training for executing experiments, record accuracy metrics in Cloud Monitoring, and use the Monitoring API to query the results.
C
Use AI Platform Training to execute experiments, store accuracy metrics in BigQuery, and query the results via the BigQuery API.
D
Implement Kubeflow Pipelines to manage and run experiments, automatically track metrics, and use the Kubeflow Pipelines API for querying results.
E
Combine the use of AI Platform Training for experiment execution and Kubeflow Pipelines for workflow management, storing metrics in both Cloud Monitoring and BigQuery for comprehensive analysis.