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Answer: 1. Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory. 2. Reference tf.data.TFRecordDataset in the training script. 3. Train the model by using Vertex AI Training with a V100 GPU.
Option A is the correct answer because it suggests using a Dataflow job to create sharded TFRecord files in a Cloud Storage directory. This approach is both efficient and scalable for handling millions of labeled images. By referencing tf.data.TFRecordDataset in the training script, you can take advantage of TensorFlow's optimized data pipeline capabilities. Additionally, using Vertex AI Training with a V100 GPU ensures that the model training process is both powerful and scalable, leveraging dedicated hardware designed for high-performance machine learning tasks. Option A provides a low-maintenance pipeline that ensures maximum efficiency and scalability, which aligns with the requirements.
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You are tasked with training an image classification model using TensorFlow. The dataset at your disposal is stored in a Cloud Storage directory and consists of millions of labeled images. Prior to commencing model training, data preparation is crucial. Your goal is to ensure that the entire data preprocessing and model training workflow is efficient, scalable, and requires minimal maintenance. Given these requirements, which approach should you take?
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