Professional Machine Learning Engineer

Question

Your team is training many ML models that use different algorithms, parameters, and datasets. Some models are trained in Vertex AI Pipelines, while others are trained on Vertex AI Workbench notebook instances. The team wants to compare model performance across both services while minimizing the effort needed to store parameters and metrics. What should you do?

Explanation

Vertex AI Experiments provides a shared experiment context for tracking and comparing parameters and metrics from both pipeline jobs and notebook-based training. Pipeline jobs can be associated with an experiment as pipeline runs, and notebook workloads can log parameters and metrics to experiment runs with the Vertex AI SDK. This avoids building and maintaining separate custom storage or metadata-tracking implementations.

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Professional Machine Learning Engineer

AstroTutor

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