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Continuous Training for Production ML in the TensorFlow Extended (TFX) Platform

Denis M. Baylor
Kevin Haas
Sammy W Leong
Rose Liu
Clemens Mewald
Neoklis Polyzotis
Mitch Trott
Marty Zinkevich
In proceedings of USENIX OpML 2019
Google Scholar


Large organizations rely increasingly on continuous ML pipelines in order to keep machine-learned models continuously up-to-date with respect to data. In this scenario, disruptions in the pipeline can increase model staleness and thus degrade the quality of downstream services supported by these models. In this paper we describe the operation of continuous pipelines in the Tensorflow Extended (TFX) platform that we developed and deployed at Google. We present the main mechanisms in TFX to support this type of pipelines in production and the lessons learned from the deployment of the platform internally at Google.

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