Google Cloud Professional Machine Learning Engineer — Question 85

You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline: ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finally deploying the model to an endpoint. You want to orchestrate the entire pipeline with minimal cluster management. What approach should you use?

Answer options

• A. Use Kubeflow Pipelines on Google Kubernetes Engine.
• B. Use Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.
• C. Use Vertex AI Pipelines with Kubeflow Pipelines SDK.
• D. Use Cloud Composer for the orchestration.

Correct answer: C

Explanation

The correct answer is C because Vertex AI Pipelines with Kubeflow Pipelines SDK provides a seamless way to orchestrate machine learning workflows with reduced management overhead. Option A involves more cluster management through Google Kubernetes Engine, while B focuses on TensorFlow Extended, which is not necessary in this context. Option D, using Cloud Composer, is more suited for orchestrating complex workflows but does not specifically cater to the streamlined ML pipeline needs as effectively as option C.