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Securing AI at the Edge: Privacy Workshop with Kubernetes

Date / Time: Friday 22 November / 11:05-12:05 EET @ Workshop room (-1st floor - Room 2)

Speaker: Dimitris Karakasilis
Open Source Principal Software Engineer @ SpectroCloud

Speaker: Mauro Morales
Open Source Software Specialist @ SpectroCloud

Workshop Description:
Artificial intelligence (AI) is increasingly pervasive in our lives, driving the need for decentralized processing at the edge. However, this convergence of AI and edge computing poses significant challenges, particularly in privacy and security.

This workshop delves into the crucial intersection of AI deployment at the edge and the imperative need for privacy-centric approaches. Through hands-on exercises and demonstrations, participants will explore how Kubernetes, a robust container orchestration platform, can facilitate the seamless deployment and maintenance of AI models, including large language models (LLMs), at the edge.
 

Drawing upon real-world use cases and best practices, this workshop aims to empower participants with practical skills and actionable insights to deploy AI securely and responsibly at the edge. Join us to learn how to navigate the complexities of AI deployment while preserving privacy in an interconnected world.

What will you Learn:
Equip tech leads and engineering managers with the knowledge and skills needed to effectively utilize analysed data extracted from git repositories for optimising software development processes, improving team dynamics, and driving continuous improvement within their teams.

Level: Intermediate

TAGs: #AI #Kubernetes #Edge #Security #Privacy

Target audience: Tech leads, Engineering managers, Developers who want to want to improve their skills in a data-driven way.

Prerequisites on Audience: 

  • A laptop and and a pre-installed way to run VMs (a hypervisor). Virtualbox or qemu/libvirt should do.  
  • Ideally the laptop should enough resources to do AI inference but it’s not required.

Deliverables: 

  • Software developed during the workshop.
  • The presentation presented during the workshop.

Schedule: 

  • Introduction to Kairos (what is Kairos, what is the demonstrated use case and a summary of what we’ll try to cover during the workshop) 
  • Deploying a Kubernetes cluster with Kairos 
  • Deploying an AI model workload on the cluster (https://localai.io/) 
  • Testing things out
  • Conclusion (what we did, why it’s important, Q&A, etc)