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 Duration 21 hours

Course Outline

Introduction to Edge AI and Kubernetes

  • The strategic role of AI in edge computing
  • Kubernetes as the orchestrator for distributed systems
  • Key use cases across various industries

Kubernetes Distributions for Edge Environments

  • Comparison of K3s, MicroK8s, and KubeEdge
  • Workflows for installation and configuration
  • Node requirements and deployment strategies

Architectures for Edge AI Deployment

  • Centralized, decentralized, and hybrid edge models
  • Resource allocation across limited-capacity nodes
  • Multi-node and remote cluster topologies

Deploying Machine Learning Models at the Edge

  • Packaging inference workloads using containers
  • Leveraging GPU and accelerator hardware where applicable
  • Managing model updates on distributed devices

Communication and Connectivity Strategies

  • Addressing intermittent and unstable network conditions
  • Synchronization techniques for edge-to-cloud data transfer
  • Message queues and protocol considerations

Observability and Monitoring at the Edge

  • Lightweight monitoring methodologies
  • Telemetry collection from remote nodes
  • Debugging distributed inference workflows

Security for Edge AI Deployments

  • Safeguarding data and models on constrained devices
  • Strategies for secure boot and trusted execution
  • Authentication and authorization mechanisms across nodes

Performance Optimization for Edge Workloads

  • Latency reduction through optimized deployment strategies
  • Storage and caching best practices
  • Compute resource tuning for inference efficiency

Summary and Next Steps

Requirements

  • Foundational knowledge of containerized applications
  • Practical experience in Kubernetes administration
  • Understanding of core edge computing concepts

Target Audience

  • IoT engineers responsible for distributed device deployments
  • Cloud-native developers constructing intelligent applications
  • Edge architects designing connected infrastructure environments

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