Q: 7
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models.
The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing
(NLP), and speech recognition. An ML engineer needs to implement a solution to organize the
existing models into these groups to improve model discoverability at scale. The solution must not
affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?
Options
Discussion
A is the way to go here since tags let you organize models by category without having to mess with their existing groupings or move anything around. Model groups already exist, so adding custom tags (like category) just lets you slice and search the registry better. I think this lines up with how AWS recommends resource grouping. Anyone see a downside?
A tbh, tagging is the AWS way to organize stuff without messing with artifact integrity or moving models around. Model groups are already in use so tags just add another filter layer. Pretty sure no need to restructure here.
A, Tagging is the best fit since it won’t disrupt any existing model group structure or affect artifacts. Just adding tags makes it easy to filter models by category later. If "model group" membership had to be exclusive per category, then B might work instead.
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