Konstantinos Katsiapis
Konstantinos (Gus) is the über tech lead of TensorFlow Extended (TFX), an end-to-end machine learning platform based on TensorFlow (tensorflow.org/tfx). Before that he worked on Sibyl, a massive scale machine learning system (precursor to TensorFlow) widely used at Google. Prior to being a builder of machine learning infrastructure he was an avid user of it, while leading the Mobile Display Ads Quality machine learning team at Google.
Prior to Google, Gus gathered knowledge and experience at Amazon, Calian, Ontario Ministry of Finance, Independent Electricity System Operator, and Computron.
Gus earned a master's degree in computer science with a specialization in artificial intelligence from Stanford University and before that a bachelor's degree in mathematics, majoring in computer science and minoring in economics, from the University of Waterloo.
Prior to Google, Gus gathered knowledge and experience at Amazon, Calian, Ontario Ministry of Finance, Independent Electricity System Operator, and Computron.
Gus earned a master's degree in computer science with a specialization in artificial intelligence from Stanford University and before that a bachelor's degree in mathematics, majoring in computer science and minoring in economics, from the University of Waterloo.
Authored Publications
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Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
Abhijit Karmarkar
Ahmet Altay
Aleksandr Zaks
Anusha Ramesh
Jarek Wilkiewicz
Jiri Simsa
Justin Hong
Mitch Trott
Neoklis Polyzotis
Noé Lutz
Robert Crowe
Sarah Sirajuddin
Zhitao Li
(2020)
Preview abstract
Software Engineering, as a discipline, has matured over the past 5+ decades. The modern world heavily depends on it, so the increased maturity of Software Engineering is a necessary blessing. Practices like testing and reliable technologies help make Software Engineering reliable enough to build industries upon. Meanwhile, Machine Learning (ML) has also grown over the past 2+ decades. ML is used more and more for research, experimentation and production workloads. ML now commonly powers widely-used products integral to our lives.
But ML Engineering, as a discipline, has not widely matured as much as its Software Engineering ancestor. Can we take what we have learned and help the nascent field of applied ML evolve into ML Engineering the way Programming evolved into Software Engineering [book]?
In this article we will give a whirlwind tour of Sibyl [article] and TensorFlow Extended (TFX) [website], two successive end-to-end (E2E) ML platforms at Alphabet. We will share the lessons learned from over a decade of applied ML built on these platforms, explain both their similarities and their differences, and expand on the shifts (both mental and technical) that helped us on our journey. In addition, we will highlight some of the capabilities of TFX that help realize several aspects of ML Engineering. We argue that in order to unlock the gains ML can bring, organizations should advance the maturity of their ML teams by investing in robust ML infrastructure and promoting ML Engineering education. We also recommend that before focusing on cutting-edge ML modeling techniques, product leaders should invest more time in adopting interoperable ML platforms for their organizations. In closing, we will also share a glimpse into the future of TFX.
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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
Preview abstract
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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