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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #346

You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. Y

The correct answer is C. 1. Create a managed pipeline in Vertex AI Pipelines to train your model by using a Vertex AI. Creating a managed pipeline in Vertex AI Pipelines that includes training (via CustomTrainingJobOp) and model registration (via ModelUploadOp) enables tracking of model versions and lineage. Scheduling the pipeline with Cloud Scheduler and Cloud Run ensures reliable, repeatable w

Submitted by anjalisingh· Apr 18, 2026ML pipeline operationalization

Question

You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. You plan to retrain your model weekly. How should you operationalize your training process?

Options

  • A
    1. Create an instance of the CustomTrainingJob class with the Vertex AI SDK to train your model.
  • B
    1. Create an instance of the CustomJob class with the Vertex AI SDK to train your model.
  • C
    1. Create a managed pipeline in Vertex AI Pipelines to train your model by using a Vertex AI
  • D
    1. Create a managed pipeline in Vertex AI Pipelines to train your model using a Vertex AI

How the community answered

(57 responses)
  • A
    4% (2)
  • B
    12% (7)
  • C
    79% (45)
  • D
    5% (3)

Explanation

Creating a managed pipeline in Vertex AI Pipelines that includes training (via CustomTrainingJobOp) and model registration (via ModelUploadOp) enables tracking of model versions and lineage. Scheduling the pipeline with Cloud Scheduler and Cloud Run ensures reliable, repeatable weekly retraining following Google best practices.

Topics

#ML Pipelines#Vertex AI Pipelines#Model Training Operationalization#Model Lineage

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