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Course Outline

Introduction to Edge AI and Kubernetes

  • Understanding the role of AI at the edge.
  • Kubernetes as an orchestrator for distributed environments.
  • Typical use cases across 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 with containers.
  • Leveraging GPU and accelerator hardware where available.
  • Managing model updates on distributed devices.

Communication and Connectivity Strategies

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

Observability and Monitoring at the Edge

  • Lightweight monitoring approaches.
  • Collecting telemetry from remote nodes.
  • Debugging distributed inference workflows.

Security for Edge AI Deployments

  • Protecting data and models on constrained devices.
  • Secure boot and trusted execution strategies.
  • Authentication and authorization across nodes.

Performance Optimization for Edge Workloads

  • Reducing latency through deployment strategies.
  • Storage and caching considerations.
  • Tuning compute resources for inference efficiency.

Summary and Next Steps

Requirements

  • A solid understanding of containerized applications.
  • Experience with Kubernetes administration.
  • Familiarity with edge computing concepts.

Audience

  • IoT engineers deploying distributed devices.
  • Cloud-native developers building intelligent applications.
  • Edge architects designing connected environments.
 21 Hours

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