Applied Data Science Invited Speakers

The Applied Data Science Invited Talks will provide a venue for leading experts in the world of applied data mining and knowledge discovery. These invited talks will feature highly influential speakers who have directly contributed to successful data mining applications in their respective fields. The talks and discussions will focus on innovative and leading-edge, large-scale industry or government applications of data mining in areas such as finance, health-care, bio-informatics, public policy, infrastructure, telecommunications, social media and computational advertising.


Keynote: Kevin Haas

Kevin Haas

Google

Towards ML Engineering with TensorFlow Extended (TFX)

The discipline of Software Engineering has evolved over the past 5+ decades to good levels of maturity. This maturity is in fact both a blessing and a necessity, since the modern world largely depends on it.

At the same time, the popularity of Machine Learning (ML) has been steadily increasing over the past 2+ decades, and over the last decade ML is being increasingly used for both experimentation and production workloads. It is no longer uncommon for ML to power widely used applications and products that are integral parts of our life. Much like what was the case for Software Engineering, the proliferation of use of ML technology necessitates the evolution of the ML discipline from “Coding” to “Engineering”.

Gus Katsiapis offers a view from the trenches of using and building end-to-end ML platforms, and shares collective knowledge and experience, gothered over more than a decade of applied ML at Google. We hope this helps pave the way towards a world of ML Engineering.

Kevin Haas offers an overview of TensorFlow Extended (TFX), the end-to-end machine learning platform for TensorFlow that powers products across all of Alphabet (and beyond). TFX helps effectively manage the end-to-end training and production workflow including model management, versioning, and serving, thereby helping one realize aspects of ML Engineering.

This is a joint talk with Konstantinos Katsiapis.


Kevin Haas is a senior engineering manager at Google Research, driving the open source adoption of Tensorflow Extended (https://github.com/tensorflow/tfx), one of Google’s production ML platforms. Kevin previously served as an engineering leader for multiple machine learning and infrastructure efforts in Google Cloud, Research, and Infrastructure teams. Prior to Google, Kevin led knowledge and search infrastructure efforts in multiple Internet and software companies, including IBM, Microsoft, and Yahoo!. Kevin received his MS from Stanford University in Computer Science in dual specializations of systems.

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