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 Duration 21 hours

Course Outline

Introduction to Edge AI and Kubernetes

  • Exploring the role of AI at the edge
  • Kubernetes as an orchestrator for distributed environments
  • Common use cases across various industries

Kubernetes Distributions for Edge Environments

  • Comparing K3s, MicroK8s, and KubeEdge
  • Installation and configuration workflows
  • Node requirements and deployment patterns

Architectures for Edge AI Deployment

  • Centralized, decentralized, and hybrid edge models
  • Resource allocation across constrained 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 available
  • Managing model updates across distributed devices

Communication and Connectivity Strategies

  • Managing intermittent and unstable network conditions
  • Techniques for synchronizing edge-to-cloud data
  • Considerations for message queues and protocols

Observability and Monitoring at the Edge

  • Lightweight approaches to monitoring
  • Gathering telemetry 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
  • Implementing authentication and authorization across nodes

Performance Optimization for Edge Workloads

  • Minimizing latency through strategic deployment
  • Storage and caching best practices
  • Optimizing compute resources for inference efficiency

Summary and Next Steps

Requirements

  • A solid grasp of containerized applications
  • Practical experience with Kubernetes administration
  • Familiarity with the fundamentals of edge computing

Target Audience

  • IoT engineers working with distributed devices
  • Cloud-native developers creating intelligent applications
  • Edge architects designing connected environments

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