You are using Vertex AI to manage your ML models and datasets. You recently updated one of your models. You want to track and compare the new version with the previous one and incorporate dataset versioning. What should you do?
A.
Use Vertex AI TensorBoard to visualize the training metrics of the new model version, and use Data Catalog to manage dataset versioning.
B.
Use Vertex AI Model Monitoring to monitor the performance of the new model version, and use Vertex AI Training to manage dataset versioning.
C.
Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex ML Metadata to manage dataset versioning.
D.
Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex AI managed datasets to manage dataset versioning.
Vertex ML Metadata funciona como una base de datos gráfica que registra estas relaciones. Cuando ejecutas un experimento, registras un "Artefacto de Dataset" (con su versión/URI) y lo vinculas a la ejecución. Esto te permite "incorporar el versionado" de manera que puedas responder: "El modelo X falló porque usó la versión antigua Y de los datos".
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