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Answer: Use Kubeflow Pipelines on Google Kubernetes Engine.
The correct answer is A: Use Kubeflow Pipelines on Google Kubernetes Engine. Although there was some debate among the community, the highly voted answer and recommended approach, especially for the given ML workflow involving video processing, is to use Kubeflow Pipelines on Google Kubernetes Engine. This approach provides the flexibility needed for custom ML workflows and automates the end-to-end process with minimal cluster management. The other options either focus on different use cases or require more hands-on infrastructure management.
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You are developing an ML model for real-time video processing that involves slicing frames from a video feed and creating bounding boxes around specific objects. Your goal is to automate the entire training pipeline on Google Cloud. This involves: ingestion and preprocessing of data stored in Cloud Storage, training and hyperparameter tuning of the object detection model using Vertex AI jobs, and deploying the model to an endpoint. To achieve this, you want to orchestrate the end-to-end pipeline with minimal cluster management. Given these requirements, what approach should you use?
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.
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