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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #326

You are building an ML pipeline to process and analyze both streaming and batch datasets. You need the pipeline to handle data validation, preprocessing, model training, and model deployment in a cons

The correct answer is D. Implement an orchestration framework such as Kubeflow Pipelines or Vertex AI Pipelines.. Vertex AI Pipelines (or Kubeflow Pipelines) are designed for orchestrating end-to-end ML workflows. They support consistent execution of validation, preprocessing, training, and deployment steps, capture metadata automatically, allow for easy reproducibility, and promote reuse of

Submitted by takeshi77· Apr 18, 2026ML pipeline operationalization

Question

You are building an ML pipeline to process and analyze both streaming and batch datasets. You need the pipeline to handle data validation, preprocessing, model training, and model deployment in a consistent and automated way. You want to design an efficient and scalable solution that captures model training metadata and is easily reproducible. You want to be able to reuse custom components for different parts of your pipeline. What should you do?

Options

  • AUse Cloud Composer for distributed processing of batch and streaming data in the pipeline.
  • BUse Dataflow for distributed processing of batch and streaming data in the pipeline.
  • CUse Cloud Build to build and push Docker images for each pipeline component.
  • DImplement an orchestration framework such as Kubeflow Pipelines or Vertex AI Pipelines.

How the community answered

(36 responses)
  • A
    3% (1)
  • B
    11% (4)
  • C
    3% (1)
  • D
    83% (30)

Explanation

Vertex AI Pipelines (or Kubeflow Pipelines) are designed for orchestrating end-to-end ML workflows. They support consistent execution of validation, preprocessing, training, and deployment steps, capture metadata automatically, allow for easy reproducibility, and promote reuse of custom components. This makes them the most efficient and scalable solution for your

Topics

#ML Pipelines#Orchestration#Reproducibility#MLOps

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