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Duration 21 hours
Course Outline
Foundations of Edge AI and Kubernetes
- Exploring the strategic role of AI at the network edge
- Leveraging Kubernetes as an orchestrator for distributed systems
- Identifying common use cases across various industries
Kubernetes Distributions Suited for Edge Environments
- Evaluating K3s, MicroK8s, and KubeEdge for edge suitability
- Standard installation and configuration procedures
- Assessing node requirements and optimal deployment patterns
Architectural Models for Edge AI Deployment
- Analyzing centralized, decentralized, and hybrid edge models
- Optimizing resource allocation on constrained nodes
- Designing multi-node and remote cluster topologies
Deployment of Machine Learning Models at the Edge
- Encapsulating inference workloads within containers
- Utilizing GPU and accelerator hardware where applicable
- Overseeing model updates across distributed device fleets
Communication and Connectivity Protocols
- Mitigating the impact of intermittent and unstable network conditions
- Implementing synchronization techniques for edge-to-cloud data flows
- Considering message queues and protocol selection strategies
Observability and Monitoring Practices at the Edge
- Adopting lightweight monitoring solutions
- Aggregating telemetry data from remote nodes
- Troubleshooting distributed inference workflows
Security Protocols for Edge AI Deployments
- Safeguarding data and models on resource-constrained devices
- Implementing secure boot and trusted execution environments
- Managing authentication and authorization across node clusters
Performance Optimization for Edge Workloads
- Minimizing latency through strategic deployment methods
- Addressing storage and caching best practices
- Tuning compute resources to maximize inference efficiency
Conclusion and Future Directions
Requirements
- A solid understanding of containerized application architectures
- Practical experience in Kubernetes administration
- Familiarity with fundamental edge computing principles
Target Audience
- IoT engineers responsible for deploying distributed device networks
- Cloud-native developers constructing intelligent, adaptive applications
- Edge architects engineering complex, connected infrastructure environments
Testimonials (2)
As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .
Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes
basic understanding of container/kubernetes and how they interact features of the openshift plattform