Jarek Wilkiewicz

Jarek Wilkiewicz

Jarek is the Director of Technical Program Management at Google DeepMind. Jarek joined Google in 2010, has a MS in Software Management from Carnegie Mellon University and BS Computer Science with concentration on AI from The University of Memphis. Prior to joining Google, he worked on building telecommunications systems software at Hewlett Packard, BEA Systems, Alcatel and a couple of startups.

Research Areas

Authored Publications
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    Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
    Abhijit Karmarkar
    Ahmet Altay
    Aleksandr Zaks
    Anusha Ramesh
    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. View details
    TFX: A TensorFlow-Based Production-Scale Machine Learning Platform
    Akshay Naresh Modi
    Chiu Yuen Koo
    Chuan Yu Foo
    Clemens Mewald
    Denis M. Baylor
    Levent Koc
    Lukasz Lew
    Martin A. Zinkevich
    Mustafa Ispir
    Neoklis Polyzotis
    Steven Whang
    Sudip Roy
    Sukriti Ramesh
    Vihan Jain
    Xin Zhang
    KDD 2017
    Preview abstract Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present TensorFlow Extended (TFX), a TensorFlow-based general-purpose machine learning platform implemented at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes disruptions. We present the case study of one deployment of TFX in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying TFX led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis. View details