Your team is training a large number of ML models gcp video
ml-engineer-pro video for your team is training a large number of ML models that use different algorithms, parameters, and datasets. Some models are trained in
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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 AI 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?