Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Introduction to Edge AI and Kubernetes
- The critical role of AI at the edge.
- Leveraging Kubernetes as an orchestrator for distributed ecosystems.
- Key industry use cases and applications.
Kubernetes Distributions for Edge Environments
- Evaluating and comparing K3s, MicroK8s, and KubeEdge.
- Best practices for installation and configuration workflows.
- Understanding node requirements and optimal deployment patterns.
Architectures for Edge AI Deployment
- Designing centralized, decentralized, and hybrid edge models.
- Strategic resource allocation across constrained nodes.
- Configuring multi-node and remote cluster topologies.
Deploying Machine Learning Models at the Edge
- Packaging inference workloads using containerization technologies.
- Utilizing GPU and accelerator hardware where available.
- Managing model updates across distributed devices.
Communication and Connectivity Strategies
- Mitigating the impact of intermittent and unstable network conditions.
- Implementing synchronization techniques for edge-to-cloud data flows.
- Considerations for message queues and network protocols.
Observability and Monitoring at the Edge
- Adopting lightweight monitoring approaches.
- Effective telemetry collection from remote nodes.
- Debugging complex distributed inference workflows.
Security for Edge AI Deployments
- Safeguarding data and models on constrained devices.
- Implementing secure boot and trusted execution strategies.
- Managing authentication and authorization across all nodes.
Performance Optimization for Edge Workloads
- Minimizing latency through strategic deployment tactics.
- Optimizing storage and caching mechanisms.
- Tuning compute resources for maximum inference efficiency.
Summary and Next Steps
Requirements
- A solid grasp of containerized application development.
- Hands-on experience with Kubernetes administration.
- Foundational knowledge of edge computing principles.
Target Audience
- IoT engineers responsible for deploying distributed device fleets.
- Cloud-native developers constructing intelligent, application-centric systems.
- Edge architects designing and managing 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
