Kursplan
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
- Using GPU and accelerator hardware when 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
Krav
- An 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
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