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Google Cloud Certified Professional Machine Learning Engineer · Google Cloud
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Convert CSV files to TFRecord format and use tf.data API TFRecord is TensorFlow’s optimized binary format for storing large datasets. It is much faster to read and parse than CSV, especially at scale. Reduces CPU overhead and improves throughput during training. dataset = tf.data.TFRecordDataset(filenames) dataset = dataset.map(parse_function, num_parallel_calls=tf.data.AUTOTUNE) dataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE)
It's C. BigTable would usually help with heavy I/O ops, but is not suited for (semi)structured data by design.
C) The most suitable option for improving input/output execution performance in this scenario is C. Convert the CSV files into shards of TFRecords and store the data in Cloud Storage. This approach leverages the efficiency of TFRecords and the scalability of Cloud Storage, aligning with TensorFlow best practices.