You are tasked with building an MLOps pipeline to retrain tree-based models in production.

Google Professional-Machine-Learning-Engineer Question Answer

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?

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.

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

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

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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Update on: Jul 30, 2025
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You are tasked with building an MLOps pipeline to retrain tree-based models in production.

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