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.