Distributed TensorFlow using Kubernetes
Distributed TensorFlow using Kubernetes

Abstract: 

In this session, attendees will learn about operationalizing TensorFlow Object Detection API using cloud services and Kubernetes.
First part will cover TensorFlow Object Detection API and how-to setup our training and evaluation workflow using Docker containers and virtual machines.

After that, attendees will learn about how to train and scale using Kubernetes and distributed TensorFlow.
Finally, session will cover how we can serve our trained model using TensorFlow Serving as a web service, and we will be deploying a simple client to get results from our service.

Bio: 

Sertaç Özercan is a software engineer in Microsoft, based in San Francisco. He works on open source projects with developer communities, and engineering teams, particularly around cloud infrastructure, containers and container orchestration. He is interested in running distributed machine learning workloads at scale using Kubernetes. He has MS degree of Computer Science from Ohio University.

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from - Youtube
Vimeo
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google