Best Strategy for Organizing Jobs, Models, and Versions on AI Platform
Question
You work on a growing team of more than 50 data scientists who all use AI Platform.
You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way.
Which strategy should you choose?
Answers
Explanations
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A. B. C. D.A.
The recommended strategy for organizing jobs, models, and versions in a clean and scalable way for a growing team of more than 50 data scientists who use AI Platform is to use labels to categorize resources.
Option A, which is to set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance, is not a scalable solution for a growing team of more than 50 data scientists. This approach may lead to a large number of IAM roles and policies, which can become unmanageable over time.
Option B, which is to separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user, can also become unmanageable over time. This approach can result in a large number of projects that can be difficult to track and maintain.
Option D, which is to set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage, can provide useful information about resource usage. However, this approach does not directly address the issue of organizing jobs, models, and versions in a clean and scalable way.
Option C, which is to use labels to organize resources into descriptive categories and apply a label to each created resource, is the recommended strategy. This approach allows users to filter the results by label when viewing or monitoring the resources. For example, labels can be used to categorize resources by project, team, or environment. This approach provides a flexible and scalable way to organize resources while allowing users to quickly and easily locate the resources they need. Additionally, labels can be used to enforce policies such as cost allocation, which can be useful for managing large-scale projects.
Overall, option C provides the most scalable and flexible approach for organizing jobs, models, and versions in a clean and scalable way for a growing team of more than 50 data scientists who use AI Platform.
