Building Scalable End-to-End Deep Learning Pipeline in the Cloud

Session Description

Machine and deep learning become essential for many companies' internal or external offerings. One of the main issues with deploying these technologies is finding the right way to train and operationalize the model within the company. A serverless approach for deep learning provides simple, scalable, affordable and reliable architecture for it. This session will demonstrate how to do so within AWS infrastructure.

Serverless architecture changes the rules of the game - instead of thinking about cluster management, scalability, and query processing, organizations can now focus entirely on training the model. The downside of this approach is having to to keep in mind certain limitations and figuring out how to organize training and deployment of the model in a correct fashion.

This session is geared towards DevOps and machine learning engineers. Attendees will learn how to deploy, train, and inference pipelines for Tensorflow and Pytorch models on serverless AWS infrastructure.