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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #140

You have been asked to productionize a proof-of-concept ML model built using Keras. The model was trained in a Jupyter notebook on a data scientist's local machine. The notebook contains a cell that…

The correct answer is B. Write the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines. The reason for this choice is that TFX and Vertex AI Pipelines provide a scalable and cost- effective solution for productionizing machine learning models. TFX is an end-to-end ML platform for building scalable and repeatable ML workflows, while Vertex AI Pipelines provides a…

Submitted by layla.eg· Apr 18, 2026ML pipeline operationalization

Question

You have been asked to productionize a proof-of-concept ML model built using Keras. The model was trained in a Jupyter notebook on a data scientist's local machine. The notebook contains a cell that performs data validation and a cell that performs model analysis. You need to orchestrate the steps contained in the notebook and automate the execution of these steps for weekly retraining. You expect much more training data in the future. You want your solution to take advantage of managed services while minimizing cost. What should you do?

Options

  • AMove the Jupyter notebook to a Notebooks instance on the largest N2 machine type, and
  • BWrite the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines.
  • CRewrite the steps in the Jupyter notebook as an Apache Spark job, and schedule the execution of
  • DExtract the steps contained in the Jupyter notebook as Python scripts, wrap each script in an

How the community answered

(42 responses)
  • A
    5% (2)
  • B
    64% (27)
  • C
    10% (4)
  • D
    21% (9)

Explanation

The reason for this choice is that TFX and Vertex AI Pipelines provide a scalable and cost- effective solution for productionizing machine learning models. TFX is an end-to-end ML platform for building scalable and repeatable ML workflows, while Vertex AI Pipelines provides a fully managed service for orchestrating ML workflows at scale. By using TFX and Vertex AI Pipelines, you can automate the execution of the steps contained in the Jupyter notebook, and schedule the pipeline for weekly retraining. This approach also takes advantage of managed services, which helps to minimize cost.

Topics

#ML Pipelines#Orchestration#Vertex AI#TFX

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