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 the Role of Kubernetes
- Exploring the strategic significance of AI at the edge.
- Leveraging Kubernetes as an orchestrator for distributed systems.
- Reviewing typical use cases across various industries.
Selecting Kubernetes Distributions for Edge Environments
- Evaluating K3s, MicroK8s, and KubeEdge.
- Streamlining installation and configuration workflows.
- Defining node requirements and optimal deployment patterns.
Designing Architectures for Edge AI Deployment
- Analyzing centralized, decentralized, and hybrid edge models.
- Allocating resources effectively across constrained nodes.
- Structuring multi-node and remote cluster topologies.
Implementing Machine Learning Models at the Edge
- Packaging inference workloads within containers.
- Utilizing GPU and accelerator hardware where available.
- Managing model updates across distributed devices.
Strategies for Communication and Connectivity
- Mitigating the impact of intermittent or unstable network conditions.
- Implementing synchronization techniques for edge-to-cloud data flows.
- Assessing message queues and protocol considerations.
Observability and Monitoring in Edge Contexts
- Adopting lightweight monitoring approaches.
- Capturing telemetry data from remote nodes.
- Debugging complex distributed inference workflows.
Securing Edge AI Deployments
- Safeguarding data and models on constrained devices.
- Implementing secure boot and trusted execution strategies.
- Managing authentication and authorization across nodes.
Performance Optimization for Edge Workloads
- Minimizing latency through targeted deployment strategies.
- Addressing storage and caching considerations.
- Tuning compute resources to maximize inference efficiency.
Conclusion and Future Directions
Requirements
- Foundational knowledge of containerized applications.
- Practical experience in Kubernetes administration.
- Working familiarity with core edge computing concepts.
Target Audience
- IoT engineers responsible for deploying distributed device fleets.
- Cloud-native developers focused on building intelligent application architectures.
- Edge architects designing and managing 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
