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?
A.
Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
B.
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.
C.
Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.
D.
Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using
ans: c
https://cloud.google.com/ai-platform/prediction/docs/resource-labels#overview_of_labels
You can add labels to your AI Platform Prediction jobs, models, and model versions, then use those labels to organize resources into categories when viewing or monitoring the resources.
For example, you can label jobs by team (such as engineering or research) and development phase (prod or test), then filter the jobs based on the team and phase.
Labels are also available on operations, but these labels are derived from the resource to which the operation applies. You cannot add or update labels on an operation.
A label is a key-value pair, where both the key and the value are custom strings that you supp
I read through this page: https://cloud.google.com/ai-platform/prediction/docs/sharing-models. This one sounds more like A. Is isn't that correct? I am not quite sure.
or maybe A is not correct because "sharing models using IAM" only applies to "manage access to resource" but this question is more like asking to "organize jobs, models, and versions". not sure if my understanding is right or not.
Creating separate projects for each data scientist would lead to significant overhead in managing resources and permissions across numerous projects, making it harder to scale and collaborate.
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