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Answer: Use Vertex AI Experiments for model artifacts and use Vertex ML Metadata for model lineage.
The question requires tracking both model artifacts and model lineage with a simple, effective, and reusable solution within Vertex AI. Option C is optimal because Vertex AI Experiments is specifically designed to track model artifacts, training runs, and experiments, while Vertex ML Metadata provides comprehensive lineage tracking of data, models, and workflows. This combination is native to Google Cloud, ensuring simplicity, effectiveness, and reusability without custom implementations. Option A is less suitable as it involves more complex integration and is not as streamlined. Option B is suboptimal because MLflow is not a native Google solution, complicating implementation. Option D is overly complex and custom, contradicting the requirement for simplicity and reusability. The community discussion supports C, with 67% consensus and explanations highlighting its native, simple, and reusable nature.
Author: LeetQuiz Editorial Team
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Your company has migrated several ML models to Google Cloud and you are now developing models in Vertex AI. You need to implement a system to track model artifacts and model lineage. Your goal is to create a simple, effective solution that can also be reused for future models. What should you do?
A
Use a combination of Vertex AI Pipelines and the Vertex AI SDK to integrate metadata tracking into the ML workflow.
B
Use Vertex AI Pipelines for model artifacts and MLflow for model lineage.
C
Use Vertex AI Experiments for model artifacts and use Vertex ML Metadata for model lineage.
D
Implement a scheduled metadata tracking solution using Cloud Composer and Cloud Run functions.
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