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Duration 21 hours
Course Outline
Foundations of Edge AI and Kubernetes
- Exploring the strategic role of AI in peripheral environments
- Utilizing Kubernetes as the orchestrator for distributed systems
- Examining typical industrial use cases and applications
Kubernetes Distributions for Edge Environments
- Evaluating K3s, MicroK8s, and KubeEdge
- Streamlining installation and configuration processes
- Assessing node specifications and optimal deployment patterns
Architectural Models for Edge AI Implementation
- Centralized, decentralized, and hybrid edge structural models
- Strategic resource allocation across limited-capacity nodes
- Designing multi-node and remote cluster topologies
Implementing Machine Learning Models at the Edge
- Containerizing inference workloads for portability
- Leveraging GPU and accelerator hardware where accessible
- Oversight of model updates across distributed device networks
Communication and Connectivity Frameworks
- Mitigating intermittent and unstable network conditions
- Advanced synchronization methods for edge-to-cloud data exchange
- Integration of message queues and protocol strategy considerations
Observability and Monitoring in Edge Scenarios
- Adopting lightweight monitoring methodologies
- Aggregating telemetry data from remote peripheral nodes
- Troubleshooting distributed inference operational flows
Security Protocols for Edge AI Deployments
- Safeguarding data and models on constrained hardware
- Implementing secure boot and trusted execution frameworks
- Managing authentication and authorization across distributed nodes
Performance Tuning for Edge Workloads
- Minimizing latency through strategic deployment tactics
- Optimizing storage and caching mechanisms
- Adjusting compute resources for maximum inference efficiency
Conclusion and Path Forward
Requirements
- Comprehensive knowledge of containerized application architectures
- Proven expertise in Kubernetes administrative tasks
- Strong grasp of foundational edge computing principles
Target Professionals
- IoT engineers responsible for managing distributed device fleets
- Cloud-native developers engineering intelligent software solutions
- Edge architects responsible for designing interconnected operational 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