Talk Intermediate

From laptop to Production: Building distributed AI application using Ray

Rejected

Session Description

The foundation of any normal application starts from our very own laptop. However, in the case of AI applications, that might not be feasible since you need more resources that your system might not be able to support. Not everyone can just buy a system for that purpose. How should one go about creating an AI application, for example, a custom-trained chatbot? To solve this issue, we can leverage Ray: an open-source unified framework for scaling AI and Python applications.

In this talk, we cover end to end build of distributed AI application.

Outline of talk

Introduction to Ray Framework
Overview of Demo Application
How Ray Helps
Show end-to-end architecture when Ray in picture
Demo
Setting up RayCluster
Preparing Data
Training Model
Serving Model
Whats next?
Dynamic Model Selection
Periodic Training
Model Composition
Q/A (5 min)

Session Categories

FOSS

Speakers

Sudhanshu Prajapati Developer Advocate | InfraCloud

Sudhanshu Prajapati is a Developer Advocate @ InfraCloud, With expertise in cloud-native application and distributed systems, he’s a goto source for hands-on knowledge. Apart from his work he contributes to open source projects like Aperture, Botkube and so on. Also the mainstream interest lies in core engineering, he also explores giving talks (various meetups like FOSS Delhi, K8s, CNCF and so on) and primer for folks to get started and share his knowledge from side projects and experience. You can ask him questions around Data, Cloud, and AI.