Google
PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #106
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
The correct answer is C. Use Vertex AI Pipelines with Kubeflow Pipelines SDK.. If you use TensorFlow in an ML workflow that processes terabytes of structured data or text data, should use TFX. For other use cases, Kubeflow. https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline
Submitted by anjalisingh· Apr 18, 2026ML pipeline operationalization
Question
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?
Options
- AUse Kubeflow Pipelines on Google Kubernetes Engine.
- BUse Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.
- CUse Vertex AI Pipelines with Kubeflow Pipelines SDK.
- DUse Cloud Composer for the orchestration.
How the community answered
(58 responses)- A7% (4)
- B14% (8)
- C74% (43)
- D5% (3)
Explanation
If you use TensorFlow in an ML workflow that processes terabytes of structured data or text data, should use TFX. For other use cases, Kubeflow. https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline
Topics
#ML pipeline orchestration#Vertex AI Pipelines#Kubeflow Pipelines SDK#Managed services
Community Discussion
No community discussion yet for this question.