GQP-3274 Using Kubernetes for Machine Learning Frameworks | Devoxx

Devoxx UK 2019
from Wednesday 8 May to Friday 10 May 2019.

   Using Kubernetes for Machine Learning Frameworks

Conference

Cloud, Containers & Infrastructure
Cloud, Containers & Infrastructure
Intermediate level

Kubernetes provides isolation, auto-scaling, load balancing, flexibility and GPU support. These features are critical to run computationally and data intensive and hard to parallelize machine learning models. Declarative syntax of Kubernetes deployment descriptors make it easy for non-operationally focused engineers to easily train machine learning models on Kubernetes. This talk will explain why and how Kubernetes is well suited for training and running your machine learning models in production. Specifically it will show how to setup a variety of open source machine learning frameworks such as TensorFlow, Apache MXNet and Pytorch on a Kubernetes cluster. The attendees will learn training, massaging and inference phases of setting up a Machine Learning framework on Kubernetes. Attendees will leave with a GitHub repo of fully working samples.

Kubernetes   Machine learning  
Subscribe to Devoxx on YouTube
Arun Gupta
Arun Gupta
From Amazon Web Services.

Arun Gupta is a Principal Technologist at Amazon Web Services. He is responsible for the Cloud Native Computing Foundation (CNCF) strategy within AWS, and participates at CNCF Board and technical meetings actively. He works with different teams at Amazon to help define their open source strategy. He has built and led developer communities for several years. He has extensive speaking experience in 40+ countries on myriad topics. Gupta also founded the Devoxx4Kids chapter in the US and continues to promote technology education among children. A prolific blogger, author of several books, an avid runner, a globe trotter, a Docker Captain, a Java Champion, a JUG leader, he is easily accessible at @arungupta.


Sign-in
Make sure to download the Android or iOS mobile schedule.