Question

Your team runs a Vertex AI Pipeline (built with the KFP SDK) that trains a model and produces a metrics artifact for each run. Product managers want a self-service way to compare the accuracy and AUC of models across many pipeline runs over time, without manually inspecting logs. You want to use built-in capabilities with minimal custom code. What should you do?

Accepted Answer

Have the training component write metrics into a Metrics artifact using log_metric() on the output artifact, then use the Vertex AI ML Metadata / Pipelines run comparison UI to compare runs.

Answer

Print the metrics to stdout in the training component and instruct product managers to grep the Cloud Logging entries for each pipeline run.

Answer

Write each run's metrics to a separate text file in a Cloud Storage bucket and share the bucket so managers can open individual files.

Answer

Store metrics only inside the trained model's saved artifact and require managers to load each model to read them.