Google Professional Machine Learning Engineer
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In a scenario where a large and continuously growing team of data scientists is using Google Cloud's AI Platform for developing and deploying machine learning models, the management of jobs, models, and versions becomes critical. The team is distributed across different regions and works on various projects simultaneously. Given the need for scalability, cost-effectiveness, and ease of management, which of the following strategies is the BEST to categorize, monitor, and manage these resources effectively? Choose the most appropriate option.
In a scenario where a large and continuously growing team of data scientists is using Google Cloud's AI Platform for developing and deploying machine learning models, the management of jobs, models, and versions becomes critical. The team is distributed across different regions and works on various projects simultaneously. Given the need for scalability, cost-effectiveness, and ease of management, which of the following strategies is the BEST to categorize, monitor, and manage these resources effectively? Choose the most appropriate option.
Explanation:
Using labels (Option D) is the most scalable and efficient method to manage resources in a growing team, as it allows for easy categorization, filtering, and monitoring of jobs, models, and versions. However, combining labels with a BigQuery sink for Cloud Logging logs (Option E) provides an enhanced level of monitoring and accountability by enabling detailed tracking of resource usage. This combination is ideal for large teams requiring both scalability and detailed oversight. Options A and C, while providing security and isolation, lack the scalability and flexibility needed for a growing and distributed team.