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 Duration 21 hours

Course Outline

Foundations of Edge AI and Kubernetes

  • The strategic role of AI in edge computing
  • Kubernetes as an orchestration layer for distributed systems
  • Key use cases spanning various sectors

Choosing Kubernetes Distributions for the Edge

  • Evaluating K3s, MicroK8s, and KubeEdge
  • Installation processes and configuration best practices
  • Node prerequisites and deployment patterns

Architectural Models for Edge AI

  • Centralized, decentralized, and hybrid edge topologies
  • Resource distribution across limited hardware
  • Multi-node and remote cluster structures

Implementing Machine Learning Models on the Edge

  • Containerizing inference workloads
  • Leveraging GPU and accelerator hardware where applicable
  • Handling model updates on distributed endpoints

Communication and Connectivity Approaches

  • Mitigating intermittent and unstable network conditions
  • Data synchronization methods between edge and cloud
  • Selection of message queues and relevant protocols

Observability and Monitoring in Edge Contexts

  • Implementing lightweight monitoring solutions
  • Gathering telemetry from remote infrastructure
  • Troubleshooting distributed inference processes

Security Considerations for Edge AI

  • Safeguarding data and models on restricted devices
  • Strategies for secure boot and trusted execution
  • Managing authentication and authorization across nodes

Optimizing Edge Workload Performance

  • Minimizing latency through strategic deployment
  • Best practices for storage and caching
  • Adjusting compute resources for inference efficiency

Conclusions and Future Directions

Requirements

  • A solid grasp of containerized application architecture
  • Practical experience in managing Kubernetes clusters
  • Acknowledgment of core edge computing principles

Target Audience

  • IoT engineers implementing distributed device networks
  • Cloud-native developers creating intelligent applications
  • Edge architects structuring connected infrastructures

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