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Update README.md #39

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Expand Up@@ -9,6 +9,10 @@ to their data as a TensorFlow graph. This is important as the user can then
incorporate the exported TensorFlow graph into their serving model, thus
avoiding skew between the served model and the training data.

@katsiapiskatsiapisNov 17, 2017

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Perhaps we should also link to the Research Blog Post for TFT? @KesterTong what do you think? Any suggestions for location?

@puneithpuneithNov 17, 2017

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In general I feel more cross-linking the better :) But here I will leave it to the esteemed reviewers.

@KesterTongKesterTongNov 19, 2017

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This README supercedes as a description of tf.Transform, so I think a link is not useful.

@KesterTongKesterTongNov 19, 2017

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I mean that this README supersedes the blog post.

@KesterTongKesterTongNov 19, 2017

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Also along the lines of more cross-linking, as far as I am aware Google Cloud ML Engine only mentions tf.Transform in the migration guide. Is this something that is being updated?

# tf.Transform Samples

@katsiapiskatsiapisNov 17, 2017

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Perhaps discuss with @KesterTong regarding what the best place for this is?

Should it be here or in the Getting Started Section or the more examples section?

@puneithpuneithNov 17, 2017

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I am trying to optimize for quick and easy discovery for samples @katsiapis@KesterTong Thoughts?

@KesterTongKesterTongNov 19, 2017

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Those samples are in the context of using Google Cloud ML Engine and also demonstrate how to use tf.Transform with Google Cloud Dataflow, while the samples in this repo give more detail on how to use tf.Transform in general.

Therefore I think that in the intro we should add a new paragraph (not a new section) here saying "tf.Transform works seamlessly with Google CloudML Engine. See *examples* for examples of using tf.Transform with Google CloudML Engine"

And in the "Getting Started" section I would add sentence along the lines of "For examples of how to run tf.Transform on Google Cloud Dataflow, see *examples*. These examples also demonstrate training with Google CloudML Engine.


You can take a look at our tf.Transform samples on [Cloud ML Engine Github](https://github.com/GoogleCloudPlatform/cloudml-samples#cpu-and-gpu) specifically [Criteo](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/criteo_tft), [Reddit](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/reddit_tft) and [Movielens](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/movielens).

**tf.Transform may introduce backwards incompatible changes before version
1.0**.

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