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PROFESSIONAL-CLOUD-ARCHITECT · Question #357

Your machine learning (ML) engineers use self-hosted Jupyter notebooks for tasks such as data preparation, model training, and fine-tuning. The operations team then deploys these models in various…

The correct answer is C. Use Vertex AI for machine learning and machine learning operations (MLOps) for model. Vertex AI provides a fully managed, scalable platform that includes managed notebooks, training, experimentation, and model registry, giving ML engineers flexibility while standardizing tooling across teams. Its integrated MLOps capabilities support collaboration and consistent…

Submitted by ngozi_ng· Mar 30, 2026Managing implementation

Question

Your machine learning (ML) engineers use self-hosted Jupyter notebooks for tasks such as data preparation, model training, and fine-tuning. The operations team then deploys these models in various environments. You want to provide maximum flexibility for ML engineers, promote collaboration with a common toolset, and leverage Google Cloud's scalability, while following Google-recommended practices. What should you do?

Options

  • AUse AutoML for machine learning and Cloud Deploy for model deployment.
  • BUse Colab Enterprise for machine learning and DevOps for model deployment.
  • CUse Vertex AI for machine learning and machine learning operations (MLOps) for model
  • DUse TensorFlow for machine learning and Cloud Deploy for model deployment.

How the community answered

(27 responses)
  • A
    4% (1)
  • B
    4% (1)
  • C
    93% (25)

Explanation

Vertex AI provides a fully managed, scalable platform that includes managed notebooks, training, experimentation, and model registry, giving ML engineers flexibility while standardizing tooling across teams. Its integrated MLOps capabilities support collaboration and consistent deployment of models into different environments, aligning with Google-recommended practices for production machine learning.

Topics

#Vertex AI#MLOps#model deployment#Colab Enterprise

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