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?
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?
Using labels to organize resources into descriptive categories is the best strategy for managing jobs, models, and versions in a scalable way. This approach allows users to filter and monitor resources effectively, which is particularly important in a team of over 50 data scientists. Labels provide a flexible and dynamic method to categorize and manage resources without creating silos or overly restrictive access permissions.
I think should be C, As IAM roles are given to the entire AI Notebook resource, not to a specific instance.
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.
https://cloud.google.com/ai-platform/prediction/docs/resource-labels#overview_of_labels (A) applies only to notebooks wich is not enough
C Resource tagging/labeling is the best way to manage ML resources for medium/big data science teams.
Restricting access is not scalable and creates silos - better to document sharable resources through tagging, hence C.
Went with C
C Although there are some questions where setting up a logging sink to BQ is the answer.
C) labels