# Is 1 week of course mostely inactual with new tf versions?

**URL:** <https://community.deeplearning.ai/t/is-1-week-of-course-mostely-inactual-with-new-tf-versions/430760>\
**Category:** Natural Language Processing in TensorFlow\
**Tags:** week-module-1\
**Created:** [August 28, 2023, 8:16pm UTC](https://community.deeplearning.ai/t/is-1-week-of-course-mostely-inactual-with-new-tf-versions/430760 "2023-08-28T20:16:01Z")\
**Posts on this page:** 1\
**Showing post:** 5

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**Author:** ![someone555777](https://avatars.discourse-cdn.com/v4/letter/s/67e7ee/32.png) [@someone555777](https://community.deeplearning.ai/u/someone555777)\
**Post date:** [September 6, 2023, 5:26pm UTC](https://community.deeplearning.ai/t/is-1-week-of-course-mostely-inactual-with-new-tf-versions/430760/5 "2023-09-06T17:26:58Z")

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additionally I found answers in [Keras documentation](https://keras.io/getting_started/intro_to_keras_for_engineers/)

> **The ideal machine learning model is end-to-end**
> 
> In general, you should seek to do data preprocessing **as part of your model** as much as possible, not via an external data preprocessing pipeline. That’s because external data preprocessing makes your models less portable when it’s time to use them in production. Consider a model that processes text: it uses a specific tokenization algorithm and a specific vocabulary index. When you want to ship your model to a mobile app or a JavaScript app, you will need to recreate the exact same preprocessing setup in the target language. This can get very tricky: any small discrepancy between the original pipeline and the one you recreate has the potential to completely invalidate your model, or at least severely degrade its performance.
> 
> It would be much easier to be able to simply export an end-to-end model that already includes preprocessing. **The ideal model should expect as input something as close as possible to raw data: an image model should expect RGB pixel values in the `[0, 255]` range, and a text model should accept strings of `utf-8` characters.** That way, the consumer of the exported model doesn’t have to know about the preprocessing pipeline.

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