Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

Deploying deep learning with TensorFlow

Ron Bodkin (Google), Brian Foo (Google)
1:30pm5:00pm Tuesday, March 6, 2018
Data engineering and architecture
Location: LL21 B Level: Intermediate

Who is this presentation for?

  • Data engineers, analytics operations, and data scientists involved with production

Prerequisite knowledge

  • A basic understanding of the Linux system and scripting

Materials or downloads needed in advance

  • A laptop with virtual machine program, such as VMware, installed and the ability to log in to a public cloud environment
  • A GitHub account (You'll get access to the course repository prior to the tutorial.)

What you'll learn

  • Learn best practices and approaches to deploy and scale deep learning models in production (and their trade-offs)


TensorFlow and Keras are popular libraries for machine learning because of their support for deep learning and GPU deployment. Join Ron Bodkin and Brian Foo to learn how to execute these libraries in production with vision and recommendation models and how to export, package, deploy, optimize, serve, monitor, and test models using Docker and TensorFlow Serving in Kubernetes.

Topics include:

  • Deep learning model production considerations: Processor type, batching, and scheduling
  • Introduction to environment and example models
  • Serving basics: An overview of TensorFlow Serving and exporting models for serving and testing
  • Robust deployment: The fundamentals of Docker and Kubernetes and dockerizing and deploying models with Kubernetes
  • Scheduling and sharing models: How to support multiple models and monitor them, whether running on a CPU, GPU, or TPU
  • Optimizing models with the XLA compiler
Photo of Ron Bodkin

Ron Bodkin


Ron Bodkin is technical director for applied artificial intelligence at Google, where he helps Global Fortune 500 enterprises unlock strategic value with AI, acts as executive sponsor for Google product and engineering teams to deliver value from AI solutions, and leads strategic initiatives working with customers and partners. Previously, Ron was vice president and general manager of artificial intelligence at Teradata; the founding CEO of Think Big Analytics (acquired by Teradata in 2014), which provides end-to-end support for enterprise big data, including data science, data engineering, advisory and managed services, and frameworks such as Kylo for enterprise data lakes; vice president of engineering at Quantcast, where he led the data science and engineer teams that pioneered the use of Hadoop and NoSQL for batch and real-time decision making; founder of enterprise consulting firm New Aspects; and cofounder and CTO of B2B applications provider C-Bridge. Ron holds a BS in math and computer science with honors from McGill University and a master’s degree in computer science from MIT.

Photo of Brian Foo

Brian Foo


Brian Foo is a senior software engineer in Google Cloud working on applied artificial intelligence, where he builds demos for Google Cloud’s strategic customers, as well as open source tutorials to improve public understanding of AI. Brian previously worked at Uber, where he trained machine learning models and built large scale training and inference pipeline for mapping and sensing/perception applications using Hadoop/Spark. Prior to that, Brian headed the real-time bidding optimization team at Rocket Fuel, where he worked on algorithms that determined millions of ads shown every second across many platforms such as web, mobile, and programmatic TV. Brian received a B.S. in EECS from Berkeley, and a Ph.D. in EE Telecommunications from UCLA.

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