Course Outline
Introduction to Edge AI and Kubernetes
- Understanding the role of AI at the edge.
- Kubernetes as an orchestrator for distributed environments.
- Typical use cases across various industries.
Kubernetes Distributions for Edge Environments
- Comparing K3s, MicroK8s, and KubeEdge.
- Installation and configuration workflows.
- Node requirements and deployment patterns.
Architectures for Edge AI Deployment
- Centralized, decentralized, and hybrid edge models.
- Resource allocation across constrained nodes.
- Multi-node and remote cluster topologies.
Deploying Machine Learning Models at the Edge
- Packaging inference workloads with containers.
- Utilizing GPU and accelerator hardware when available.
- Managing model updates on distributed devices.
Communication and Connectivity Strategies
- Handling intermittent and unstable network conditions.
- Synchronization techniques for edge-to-cloud data.
- Message queues and protocol considerations.
Observability and Monitoring at the Edge
- Lightweight monitoring approaches.
- Collecting telemetry from remote nodes.
- Debugging distributed inference workflows.
Security for Edge AI Deployments
- Protecting data and models on constrained devices.
- Secure boot and trusted execution strategies.
- Authentication and authorization across nodes.
Performance Optimization for Edge Workloads
- Reducing latency through deployment strategies.
- Storage and caching considerations.
- Tuning compute resources for inference efficiency.
Summary and Next Steps
Requirements
- A solid understanding of containerized applications.
- Prior experience with Kubernetes administration.
- Familiarity with the concepts of edge computing.
Target Audience
- IoT engineers responsible for deploying distributed devices.
- Cloud-native developers building intelligent applications.
- Edge architects designing connected environments.
Testimonials (2)
About the microservices and how to maintenance kubernetes
Yufri Isnaini Rochmat Maulana - Bank Indonesia
Course - Advanced Platform Engineering: Scaling with Microservices and Kubernetes
The training met expectations with its clear explanations, real-world examples, and hands-on labs that made complex topics easy to understand. It provided valuable insights into container orchestration, security, scaling and many other advanced topics.
