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Professional Machine Learning Engineer

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Topic 1 Question 15
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
- Create a tf.data.Dataset.prefetch transformation.
- Convert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().
- Convert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().
- Convert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data API to read the images for training.
解説
Reference: https://www.tensorflow.org/api_docs/python/tf/data/Dataset

ユーザの投票
コメント(17)
- Should be D
  
  👍 18
  chohan2021/06/15
- My option is D.
  
  Cite from Google Pag: to construct a Dataset from data in memory, use tf.data.Dataset.from_tensors() or tf.data.Dataset.from_tensor_slices(). When input data is stored in a file (not in memory), the recommended TFRecord format, you can use tf.data.TFRecordDataset().
  
  tf.data.Dataset is for data in memory. tf.data.TFRecordDataset is for data in non-memory storage.
  
  👍 10
  alphard2021/12/07
- TFRecord for non-memeory
  
  👍 4
  stefant2022/01/19
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