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Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Question # 87 Topic 9 Discussion

Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Question # 87 Topic 9 Discussion

Professional-Machine-Learning-Engineer Exam Topic 9 Question 87 Discussion:
Question #: 87
Topic #: 9

You are tasked with building an MLOps pipeline to retrain tree-based models in production. The pipeline will include components related to data ingestion, data processing, model training, model evaluation, and model deployment. Your organization primarily uses PySpark-based workloads for data preprocessing. You want to minimize infrastructure management effort. How should you set up the pipeline?


A.

Set up a TensorFlow Extended (TFX) pipeline on Vertex Al Pipelines to orchestrate the MLOps pipeline. Write a custom component for the PySpark-based workloads on Dataproc.


B.

Set up a Vertex Al Pipelines to orchestrate the MLOps pipeline. Use the predefined Dataproc component for the PySpark-based workloads.


C.

Set up Cloud Composer to orchestrate the MLOps pipeline. Use Dataproc workflow templates for the PySpark-based workloads in Cloud Composer.


D.

Set up Kubeflow Pipelines on Google Kubernetes Engine to orchestrate the MLOps pipeline. Write a custom component for the PySpark-based workloads on Dataproc.


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