Course Outline

Introduction to Edge AI and Kubernetes for Government

  • 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 for Government

  • Comparing K3s, MicroK8s, and KubeEdge
  • Installation and Configuration Workflows
  • Node Requirements and Deployment Patterns

Architectures for Edge AI Deployment for Government

  • Centralized, Decentralized, and Hybrid Edge Models
  • Resource Allocation Across Constrained Nodes
  • Multi-Node and Remote Cluster Topologies

Deploying Machine Learning Models at the Edge for Government

  • Packaging Inference Workloads with Containers
  • Using GPU and Accelerator Hardware When Available
  • Managing Model Updates on Distributed Devices

Communication and Connectivity Strategies for Government

  • Handling Intermittent and Unstable Network Conditions
  • Synchronization Techniques for Edge-to-Cloud Data
  • Message Queues and Protocol Considerations

Observability and Monitoring at the Edge for Government

  • Lightweight Monitoring Approaches
  • Collecting Telemetry from Remote Nodes
  • Debugging Distributed Inference Workflows

Security for Edge AI Deployments for Government

  • Protecting Data and Models on Constrained Devices
  • Secure Boot and Trusted Execution Strategies
  • Authentication and Authorization Across Nodes

Performance Optimization for Edge Workloads for Government

  • Reducing Latency Through Deployment Strategies
  • Storage and Caching Considerations
  • Tuning Compute Resources for Inference Efficiency

Summary and Next Steps for Government

Requirements

  • Knowledge of containerized applications
  • Experience in Kubernetes administration
  • Understanding of edge computing principles

Audience for Government

  • IoT engineers responsible for deploying distributed devices
  • Cloud-native developers working on intelligent applications
  • Edge architects designing connected environments
 21 Hours

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