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In your role as a Machine Learning Engineer at an industrial manufacturing company, you are leading a project to enhance the quality control system by implementing a deep learning model to identify semi-finished products that should be discarded. The model, built with TensorFlow, processes images captured from various stages of the production line. The images are currently stored in CSV files as binary data, presenting challenges in efficient data import and management. Given the constraints of high data volume, the need for fast processing speeds, and the importance of maintaining data integrity, what is the optimal solution for importing and managing this binary image data? Choose the best option.
A
Utilize tf.train.Feature for specifying features in Graph-based Neural Structured Learning models.
B
Implement tf.TFRecordReader to efficiently read binary data stored in TFRecord format, leveraging Protocol buffers for structured data serialization.
C
Apply tf.quantization to reduce latency and processing time, though primarily beneficial for CPU and TPU optimizations.
D
Use tf.RaggedTensor for handling tensors with varying dimension lengths, which is not directly related to binary data storage.