Scaling TensorFlow to 300 million predictions per second

Jan Hartman; Davorin Kopič

doi:10.1145/3460231.3474605

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arXiv · 2109.09541

Scaling TensorFlow to 300 million predictions per second

Jan Hartman·Davorin Kopič

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Abstract

We present the process of transitioning machine learning models to the TensorFlow framework at a large scale in an online advertising ecosystem. In this talk we address the key challenges we faced and describe how we successfully tackled them; notably, implementing the models in TF and serving them efficiently with low latency using various optimization techniques.

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Jan Hartman, Davorin Kopič. 2021-09-20. Scaling TensorFlow to 300 million predictions per second. https://doi.org/10.1145/3460231.3474605

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