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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #235

You are building a MLOps platform to automate your company's ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines. How should you store the pipelines'…

The correct answer is C. Store parameters in Vertex ML Metadata, store the models' source code in GitHub, and store the. For an MLOps platform managing dozens of pipelines, store parameters in Vertex ML Metadata, model source code in GitHub, and model binaries in Cloud Storage.

Submitted by mike_84· Apr 18, 2026ML pipeline operationalization

Question

You are building a MLOps platform to automate your company's ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines. How should you store the pipelines' artifacts?

Options

  • AStore parameters in Cloud SQL, and store the models' source code and binaries in GitHub.
  • BStore parameters in Cloud SQL, store the models' source code in GitHub, and store the models'
  • CStore parameters in Vertex ML Metadata, store the models' source code in GitHub, and store the
  • DStore parameters in Vertex ML Metadata and store the models' source code and binaries in

How the community answered

(20 responses)
  • B
    10% (2)
  • C
    85% (17)
  • D
    5% (1)

Why each option

For an MLOps platform managing dozens of pipelines, store parameters in Vertex ML Metadata, model source code in GitHub, and model binaries in Cloud Storage.

AStore parameters in Cloud SQL, and store the models' source code and binaries in GitHub.

Cloud SQL is a relational database and is not optimized for the specialized needs of tracking ML experiment metadata and parameters, which Vertex ML Metadata is designed for.

BStore parameters in Cloud SQL, store the models' source code in GitHub, and store the models'

Cloud SQL is not the appropriate service for managing ML experiment parameters and metadata due to its general-purpose nature compared to specialized ML Metadata services.

CStore parameters in Vertex ML Metadata, store the models' source code in GitHub, and store theCorrect

Vertex ML Metadata is purpose-built for tracking ML experiment parameters, runs, and artifact lineage. GitHub is the industry standard for version control of source code, and Cloud Storage provides scalable and cost-effective storage for large model binaries and checkpoints.

DStore parameters in Vertex ML Metadata and store the models' source code and binaries in

Storing large model binaries and checkpoints directly in GitHub is impractical due to repository size limitations and inefficient version control for large files, making Cloud Storage the preferred solution.

Concept tested: MLOps artifact management

Source: https://cloud.google.com/vertex-ai/docs/ml-metadata/overview

Topics

#MLOps#Artifact Management#Vertex ML Metadata#Google Cloud Platform

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