TensorFlow Extended: An End-to-End Machine Learning Platform for TensorFlow - Databricks

TensorFlow Extended: An End-to-End Machine Learning Platform for TensorFlow

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As machine learning evolves from experimentation to serving production workloads, so does the need to effectively manage the end-to-end training and production workflow including model management, versioning, and serving. Clemens Mewald offers an overview of TensorFlow Extended (TFX), the end-to-end machine learning platform for TensorFlow that powers products across all of Alphabet. Many TFX components rely on the Beam SDK to define portable data processing workflows. This talk motivates the development of a Spark runner for Beam Python.



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About 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 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.

About Ahmet Altay

Ahmet Altay is a Senior Software Engineer at Google working on Apache Beam (PMC member) and Cloud Dataflow. Previously he worked at Microsoft on operating systems. He has a master's degree from Stanford University.