Implement strict IAM permissions on AI Platform notebooks to restrict access to individual users or specific groups, potentially hindering collaboration.

Create separate Google Cloud projects for each data scientist to isolate their jobs, models, and versions, which may lead to management overhead and increased costs.

Use labels to categorize resources with descriptive key-value pairs, allowing for flexible filtering and monitoring based on team, project phase, or other criteria.

Set up a BigQuery sink for Cloud Logging logs to track AI Platform resource usage and create a SQL view in BigQuery to associate users with their resources, enabling detailed usage reports.

Combine the use of labels for resource categorization and a BigQuery sink for logging and monitoring, providing both organizational flexibility and detailed usage insights.