October 28–31, 2019
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Maximizing the performance and longevity of your TensorFlow applications on Google Cloud Platform (sponsored by Google Cloud)

Karthik Ramachandran (Google Cloud), Kaz Sato (Google)
2:30pm3:10pm Wednesday, October 30, 2019
Location: Grand Ballroom G

Karthik Ramachandran and Kaz Sato take a deep dive into how to use AI platform notebooks and deep learning virtual machines and containers to build, train, and deploy TensorFlow models on Google Cloud.

In particular, they focus on how to get up and running quickly using AI platform notebooks to set up a Juypter-based development environment, how these environments make it easy for you to take advantage of the GPUs offered by Google Cloud Platform (GCP) and some tips on ensuring you’re getting the most out of GPUs, how to improve your training performance by alleviating I/O bottlenecks through TensorFlow I/O, how to use DL containers to work across on-premises and cloud environments by running DL containers both locally and on Google Kubernetes Engine (GKE), and how to ensure the longevity of your applications.

This session is sponsored by Google Cloud.

What you'll learn

  • Learn how to use AI platform notebooks and deep learning virtual machines

Karthik Ramachandran

Google Cloud

Karthik Ramachandran is a product manager on the Cloud AI team at Google. He focuses on AI platform notebooks, deep learning virtual machines (VMs), and deep learning containers. Previously, he was a software engineer, engineering manager, and product manager at a number of different organizations including In-Q-Tel’s Lab41, Premise, and Primer.ai. Karthik has a master’s degree from Georgetown University and a bachelor’s degree from Carnegie Mellon University.

Photo of Kaz Sato

Kaz Sato


Kaz Sato is a staff developer advocate on the cloud platform team at Google, where he leads the developer advocacy team for machine learning and data analytics products such as TensorFlow, the Vision API, and BigQuery. Kaz has been leading and supporting developer communities for Google Cloud for over seven years. He’s a frequent speaker at conferences, including Google I/O 2016, Hadoop Summit 2016 San Jose, Strata + Hadoop World 2016, and Google Next 2015 NYC and Tel Aviv, and he has hosted FPGA meetups since 2013.

  • O'Reilly
  • TensorFlow
  • Google Cloud
  • IBM
  • Databricks
  • Tensor Networks
  • VMware
  • Amazon Web Services
  • One Convergence
  • Quantiphi
  • Lambda Labs
  • Tech Mahindra
  • cnvrg.io
  • Determined AI
  • Inferencery
  • Manceps, Inc.
  • PerceptiLabs
  • Valohai

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