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Question 211 - Professional Machine Learning Engineer discussion

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Your team is training a large number of ML models that use different algorithms, parameters and datasets. Some models are trained in Vertex Ai Pipelines, and some are trained on Vertex Al Workbench notebook instances. Your team wants to compare the performance of the models across both services. You want to minimize the effort required to store the parameters and metrics What should you do?

A.
Implement an additional step for all the models running in pipelines and notebooks to export parameters and metrics to BigQuery.
Answers
A.
Implement an additional step for all the models running in pipelines and notebooks to export parameters and metrics to BigQuery.
B.
Create a Vertex Al experiment Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex Al SDK.
Answers
B.
Create a Vertex Al experiment Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex Al SDK.
C.
Implement all models in Vertex Al Pipelines Create a Vertex Al experiment, and associate all pipeline runs with that experiment.
Answers
C.
Implement all models in Vertex Al Pipelines Create a Vertex Al experiment, and associate all pipeline runs with that experiment.
D.
Store all model parameters and metrics as mode! metadata by using the Vertex Al Metadata API.
Answers
D.
Store all model parameters and metrics as mode! metadata by using the Vertex Al Metadata API.
Suggested answer: B

Explanation:

Vertex AI Experiments is a service that allows you to track, compare, and manage experiments with Vertex AI. You can use Vertex AI Experiments to record the parameters, metrics, and artifacts of each model training run, and compare them in a graphical interface. Vertex AI Experiments supports models trained in Vertex AI Pipelines, Vertex AI Custom Training, and Vertex AI Workbench notebooks. To use Vertex AI Experiments, you need to create an experiment and submit your pipeline runs or custom training jobs as experiment runs. For models trained on notebooks, you need to use the Vertex AI SDK to log the parameters and metrics to the experiment. This way, you can minimize the effort required to store and compare the model performance across different services.Reference:Track, compare, manage experiments with Vertex AI Experiments,Vertex AI Pipelines: Metrics visualization and run comparison using the KFP SDK, [Vertex AI SDK for Python]

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asked 18/09/2024
Lukasz Malaczek
32 questions
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