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[D] Can I use tf.data to calculate new features as part of a pipeline, or should this be done before using the tf.data module?

I am just curious about how much of the data processing process I can refactor into a tf.data pipeline for inputting my data into my model. My source data is used to calculate different features to create a dataset, and then this dataset is processed further for inputting into my models. So the process is basically like this:

Source Data (structured JSON which just has text fields for data parsed from a raw document) —>
Dataset (this fields are used to calculate numerical features, categorical features, and sequence features) —>
Processed Dataset (standard techniques – scaling, encoding, tokenization, padding, etc.)

And then I have my input data for the model. I am wondering whether I can refactor this entire process into a tf.data pipeline, or will the tf.data pipeline only handle the processing done in the second step described above? I am using TF 2.0 Beta by the way.

Any insights or help will be greatly appreciated.

submitted by /u/that_one_ai_nerd
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.