You are building an ML model in a Vertex AI Workbench notebook. You want to track artifacts and compare models while experimenting with different approaches. As you iterate on the model implementation, you need to move successful experiments to production quickly and easily. What should you do?
A
Initialize the Vertex SDK with the name of your experiment. Log parameters and metrics for each experiment, and attach dataset and model artifacts as inputs and outputs to each execution.2. After a successful experiment create a Vertex AI pipeline.
B
Initialize the Vertex SDK with the name of your experiment. Log parameters and metrics for each experiment, save your dataset to a Cloud Storage bucket, and upload the models to Vertex AI Model Registry.2. After a successful experiment, create a Vertex AI pipeline.
C
Create a Vertex AI pipeline with parameters you want to track as arguments to your PipelineJob. Use the Metrics, Model, and Dataset artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline.2. Associate the pipeline with your experiment when you submit the job.
D
Create a Vertex AI pipeline. Use the Dataset and Model artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline.2. In your training component, use the Vertex AI SDK to create an experiment run. Configure the log_params and log_metrics functions to track parameters and metrics of your experiment.
Show Answer Answer Explanation Vertex AI Pipelines packages the successful workflow into a repeatable production-ready pipeline. Defining parameters on the PipelineJob and using Kubeflow Pipelines Dataset, Model, and Metrics artifacts as component inputs and outputs preserves lineage and records comparable run results. Associating the submitted pipeline run with a Vertex AI Experiment allows the experiment to track and compare those pipeline runs.
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