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You are tasked with classifying customer support emails using TensorFlow Estimators on your on-premises system with small datasets. Your goal is to transition this workload to Google Cloud with minimal code changes and infrastructure overhead, while ensuring high performance and scalability for potential future dataset growth. Considering cost efficiency, ease of transition, and the ability to scale, which of the following strategies should you adopt? (Choose one correct option)
A
Set up a Hadoop cluster on Google Cloud Dataproc for training purposes, leveraging its compatibility with TensorFlow.
B
Deploy your model on a Managed Instance Group with autoscaling capabilities to handle varying loads, ensuring high availability.
C
Utilize Google Cloud AI Platform for distributed training tasks, taking advantage of its managed services for machine learning workflows.
D
Implement Kubeflow Pipelines to orchestrate your training jobs on a Google Kubernetes Engine cluster, enabling containerized workflows.