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
- Exploring the significance of AI implementation at the edge.
- Utilizing Kubernetes as the orchestrator for distributed systems.
- Examining industry-specific use cases and applications.
Kubernetes Distributions for Edge Environments
- Evaluating K3s, MicroK8s, and KubeEdge.
- Walkthroughs of installation and configuration processes.
- Analyzing node requirements and optimal deployment patterns.
Architectures for Edge AI Deployment
- Designing centralized, decentralized, and hybrid edge models.
- Allocating resources efficiently across constrained nodes.
- Structuring multi-node and remote cluster topologies.
Deploying Machine Learning Models at the Edge
- Containerizing inference workloads for seamless integration.
- Leveraging GPU and accelerator hardware where available.
- Managing model updates across distributed device networks.
Communication and Connectivity Strategies
- Mitigating intermittent and unstable network conditions.
- Implementing synchronization techniques for edge-to-cloud data flow.
- Considering message queues and protocol optimizations.
Observability and Monitoring at the Edge
- Adopting lightweight monitoring solutions.
- Gathering telemetry data from remote nodes.
- Debugging complex distributed inference workflows.
Security for Edge AI Deployments
- Safeguarding data and models on resource-limited devices.
- Implementing secure boot and trusted execution environments.
- Managing authentication and authorization across edge nodes.
Performance Optimization for Edge Workloads
- Minimizing latency through strategic deployment methods.
- Optimizing storage and caching mechanisms.
- Tuning compute resources to maximize inference efficiency.
Summary and Next Steps
Requirements
- A foundational understanding of containerized application architectures.
- Practical experience in Kubernetes administration.
- A working knowledge of edge computing principles.
Target Audience
- IoT engineers responsible for deploying distributed device fleets.
- Cloud-native developers crafting intelligent applications.
- Edge architects designing scalable connected 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
