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This talk will focus on creating a production machine learning pipeline using TFX. Using TFX developers can implement machine learning pipelines capable of processing large datasets for both modeling and inference. In addition to data wrangling and feature engineering over large datasets, TFX enables detailed model analysis and versioning. The talk will focus on implementing a TFX pipeline and a discussion of current topics in model understanding.

Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol
TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM
Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions
Learn more on the I/O Website → https://google.com/io

Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1
Get started at → https://www.tensorflow.org/

Speaker(s): Kevin Haas , Tulsee Doshi , Konstantinos Katsiapis

T02F52 event: Google I/O 2019; re_ty: Publish; product: TensorFlow – TensorFlow Extended; fullname: Tulsee Doshi;

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Comments

Jugs Ma马家杰 says:

"Learning from the pitfall of others is very much important to build your own system"

Bin Wang says:

pickled tree in a flask, up and running less than an hour 🙂

bumbum says:

Where’s the slide view? Oh saw a quick pullback 🙂

ThreeCube says:

This presents more ways to lock-in yourself to Tensorflow.

Q says:

Is there sample code available for all of this somewhere?

Michael Mantion says:

zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz

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