Edge AI on Kubernetes Training in Astana, Kazakhstan

The Edge AI on Kubernetes Training is a hands-on, industry-focused program that teaches how to design, deploy, and manage AI workloads at the edge using Kubernetes.
The course covers containerized AI models, edge orchestration, scalability, observability, and secure deployment for real-world use cases such as IoT, smart manufacturing, video analytics, autonomous systems, and real-time inference.

Participants learn how to combine edge computing principles with cloud-native Kubernetes orchestration to build low-latency, resilient AI systems.

Introduction to Edge AI & Kubernetes

• What is Edge AI and why it matters

• Edge vs cloud AI workloads

• Role of Kubernetes in Edge AI

• Common Edge AI use cases and challenges

Edge Computing Architecture Fundamentals

• Edge nodes, gateways, and devices

• Network constraints and offline scenarios

• Latency-sensitive AI inference

• Data locality and processing strategies

Kubernetes for Edge Environments

• Kubernetes architecture recap

• Lightweight Kubernetes distributions

• Single-node vs multi-node edge clusters

• Cluster lifecycle management at the edge

Containerizing AI Models

• Packaging AI inference services with Docker

• Model formats and runtime considerations

• Optimizing container images for edge

• Versioning and rollback strategies

Deploying AI Inference on Kubernetes

• Deployments, Pods, and Services

• ConfigMaps and Secrets for AI apps

• Model configuration and environment setup

• Rolling updates and zero-downtime deployment

Resource Management & Optimization

• CPU, memory, and storage constraints

• GPU and accelerator scheduling

• Node affinity and taints

• Performance tuning for edge inference

Data Handling & Messaging at the Edge

• Edge data ingestion patterns

• Event-driven AI inference

• Messaging and streaming basics

• Handling intermittent connectivity

Scaling & Reliability Patterns

• Horizontal and vertical scaling

• Load balancing for edge inference

• Failover and self-healing mechanisms

• Managing unreliable edge nodes

Security & Access Control

• Securing edge Kubernetes clusters

• Secrets management and encryption

• Network policies and isolation

• Secure model distribution

Observability & Monitoring

• Logging and metrics at the edge

• Monitoring AI inference performance

• Health checks and alerting

• Troubleshooting edge deployments

CI/CD & Lifecycle Management

• CI/CD pipelines for Edge AI

• Model updates and redeployment

• Configuration and version control

• Managing fleets of edge clusters

Hands-on Exercises

Summary and Conclusion