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Duration 21 hours
Course Outline
Introduction to Edge AI Integration with Kubernetes
- Defining the strategic role of artificial intelligence at the edge
- Kubernetes as an orchestration layer for distributed infrastructure
- Common operational use cases across public and private sectors
Kubernetes Distributions for Edge Environments
- Comparison of K3s, MicroK8s, and KubeEdge capabilities
- Standardized installation and configuration procedures
- Hardware prerequisites and deployment methodologies
Architectures for Edge AI Deployment
- Centralized, decentralized, and hybrid edge architectures
- Resource management across resource-constrained nodes
- Multi-node and remote cluster structural designs
Deploying Machine Learning Models at the Edge
- Containerization of inference workloads
- Utilization of GPU and accelerator hardware where applicable
- Model update management on distributed devices
Communication and Connectivity Strategies
- Mitigation of intermittent and unstable network conditions
- Data synchronization techniques between edge and cloud environments
- Message queue implementation and protocol standards
Observability and Monitoring at the Edge
- Implementation of lightweight monitoring solutions
- Telemetry collection from remote nodes
- Diagnostic procedures for distributed inference processes
Security for Edge AI Deployments
- Data and model protection on constrained devices
- Secure boot and trusted execution mechanisms
- Authentication and authorization frameworks for node access
Performance Optimization for Edge Workloads
- Latency reduction through strategic deployment
- Storage and caching best practices
- Compute resource tuning for inference efficiency
Summary and Next Steps
Requirements
- Knowledge of containerized application architectures
- Proficiency in Kubernetes administration
- Understanding of edge computing principles
Target Audience
- IoT engineers responsible for distributed device management
- Cloud-native developers constructing intelligent applications
- Edge architects designing connected operational environments
Testimonials (1)
I like the Kubernetes part of the training as I had some tasks with K8s pods and ports and it was nice to get a better understanding now. Thank you!