October 20, 2019

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TensorFlow: Large-scale analytics and distributed machine learning with TensorFlow, BigQuery, and Dataflow (Apache Beam)

TensorFlow: Large-scale analytics and distributed machine learning with TensorFlow, BigQuery, and Dataflow (Apache Beam)

Kazunori Sato and Amy Unruh explore how you can use TensorFlow to drive large-scale distributed machine learning against your analytic data sitting in Google BigQuery, with data preprocessing driven by Dataflow (now Apache Beam). Kazunori and Amy dive into practical examples of how these technologies can work together to enable a powerful workflow for distributed machine learning.

Talk Title TensorFlow: Large-scale analytics and distributed machine learning with TensorFlow, BigQuery, and Dataflow (Apache Beam)
Speakers Kaz Sato (Google), Amy Unruh (Google)
Conference Strata + Hadoop World
Conf Tag Big Data Expo
Location San Jose, California
Date March 29-31, 2016
URL Talk Page
Slides Talk Slides
Video

TensorFlow is an open source software library for machine learning, based on previous generations of software within Google for training and deploying neural networks. BigQuery is Google’s fully managed, low-cost analytics data warehouse, which lets you do interactive queries on petabyte-sized datasets. Google Cloud Dataflow (now Beam, an Apache incubator project) is a unified programming model and service for developing and executing a wide range of data processing and analytics patterns. Together, they enable a powerful workflow for distributed machine learning. Kazunori Sato and Amy Unruh describe these technologies and explain how they work together. Kazunori and Amy offer practical examples of how you can use them to empower large-scale distributed training of neural networks and how you can use the trained models for prediction. They’ll also demonstrate how to use Google machine-learning APIs to make ML accessible to everybody. This session is sponsored by Google.

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