Course Outline
Introduction to Edge AI and Kubernetes for Government
- Understanding the Role of AI at the Edge in Public Sector Operations
- Kubernetes as an Orchestrator for Distributed Environments in Government
- Typical Use Cases Across Industries Relevant to Public Sector Applications
Kubernetes Distributions for Edge Environments for Government
- Comparing K3s, MicroK8s, and KubeEdge for Government Use
- Installation and Configuration Workflows for Government Systems
- Node Requirements and Deployment Patterns for Government Operations
Architectures for Edge AI Deployment in Government
- Centralized, Decentralized, and Hybrid Edge Models for Government Applications
- Resource Allocation Across Constrained Nodes for Efficient Government Services
- Multi-Node and Remote Cluster Topologies for Enhanced Government Operations
Deploying Machine Learning Models at the Edge for Government
- Packaging Inference Workloads with Containers for Government Systems
- Utilizing GPU and Accelerator Hardware When Available in Government Environments
- Managing Model Updates on Distributed Devices for Continuous Improvement in Government Services
Communication and Connectivity Strategies for Government Edge AI Deployments
- Handling Intermittent and Unstable Network Conditions in Government Networks
- Synchronization Techniques for Edge-to-Cloud Data in Government Systems
- Message Queues and Protocol Considerations for Efficient Government Operations
Observability and Monitoring at the Edge for Government
- Lightweight Monitoring Approaches for Efficient Government Oversight
- Collecting Telemetry from Remote Nodes to Enhance Government Accountability
- Debugging Distributed Inference Workflows for Reliable Government Services
Security for Edge AI Deployments in Government
- Protecting Data and Models on Constrained Devices in Government Systems
- Secure Boot and Trusted Execution Strategies for Government Applications
- Authentication and Authorization Across Nodes to Ensure Government Security
Performance Optimization for Edge Workloads in Government
- Reducing Latency Through Deployment Strategies for Efficient Government Services
- Storage and Caching Considerations for Optimal Government Operations
- Tuning Compute Resources for Inference Efficiency to Enhance Government Performance
Summary and Next Steps for Government
Requirements
- An understanding of containerized applications for government use.
- Experience with Kubernetes administration in a public sector environment.
- Familiarity with edge computing concepts relevant to governmental operations.
Audience
- IoT engineers deploying distributed devices for government agencies.
- Cloud-native developers building intelligent applications for government systems.
- Edge architects designing connected environments for government infrastructure.
Testimonials (5)
Interactivity, no reading slides all day
Emilien Bavay - IRIS SA
Course - Kubernetes Advanced
he was patience and understood that we fall behind
Albertina - REGNOLOGY ROMANIA S.R.L.
Course - Deploying Kubernetes Applications with Helm
The training was more practical
Siphokazi Biyana - Vodacom SA
Course - Kubernetes on AWS
Learning about Kubernetes.
Felix Bautista - SGS GULF LIMITED ROHQ
Course - Kubernetes on Azure (AKS)
It gave a good grounding for Docker and Kubernetes.
