Use Case Track  

TensorFlow Extended: An end-to-end machine learning platform for TensorFlow

 

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. TFX together with Apache Beam and Apache Flink unlocks new and exciting use cases. Robert Crowe 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 explores how Apache Flink runner for Apache Beam Python enables TFX pipelines for production ready machine learning workloads.

Authors

Robert Crowe
Robert Crowe
Google

Robert Crowe

A data scientist and TensorFlow addict, Robert has a passion for helping developers quickly learn what they need to be productive. He's used TensorFlow since the very early days and is excited about how it's evolving quickly to become even better than it already is. Before moving to data science Robert led software engineering teams for both large and small companies, always focusing on clean, elegant solutions to well-defined needs. In his spare time Robert sails, surfs occasionally, and raises a family.