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 Duration 21 hours

Course Outline

Foundations of Hybrid AI Deployment

  • Exploring hybrid, cloud, and edge deployment models
  • Analyzing AI workload characteristics and infrastructure constraints
  • Selecting the optimal deployment topology

Containerizing AI Workloads with Docker

  • Creating GPU and CPU-based inference containers
  • Managing secure images and container registries
  • Establishing reproducible environments for AI applications

Deploying AI Services to Cloud Environments

  • Executing inference on AWS, Azure, and GCP using Docker
  • Provisioning cloud compute resources for model serving
  • Securing cloud-based AI endpoints

Edge and On-Premise Deployment Strategies

  • Running AI on IoT devices, gateways, and microservers
  • Utilizing lightweight runtimes for edge environments
  • Handling intermittent connectivity and ensuring local persistence

Hybrid Networking and Secure Connectivity

  • Implementing secure tunneling between edge and cloud nodes
  • Managing certificates, secrets, and token-based access control
  • Tuning performance for low-latency inference

Orchestrating Distributed AI Deployments

  • Employing K3s, K8s, or lightweight orchestration for hybrid setups
  • Implementing service discovery and workload scheduling
  • Automating multi-location rollout strategies

Monitoring and Observability Across Environments

  • Tracking inference performance across multiple locations
  • Implementing centralized logging for hybrid AI systems
  • Detecting failures and triggering automated recovery

Scaling and Optimizing Hybrid AI Systems

  • Scaling edge clusters and cloud nodes effectively
  • Optimizing bandwidth usage and caching strategies
  • Balancing compute loads between cloud and edge resources

Summary and Next Steps

Requirements

  • A solid grasp of containerization principles
  • Proficiency with Linux command-line operations
  • Familiarity with AI model deployment processes

Target Audience

  • Infrastructure architects
  • Site Reliability Engineers (SREs)
  • Edge and IoT developers

Number of participants


Price per participant

Open Training Courses require 5+ participants.

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

multi-tiered, structured course programme.

Bert Paelinckx - Cube SoftwareSolutions
Course - Introduction to Docker

Upcoming Courses

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-11-17 10:00
21 hours
Seoul - Gran Seoul
₩ 7500000 (Online)
₩ 15000000 (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-01 10:00
21 hours
Seoul Center 1
₩ 7500000 (Online)
₩ 15000000 (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-15 10:00
21 hours
Jungang-Dong Center
₩ 7500000 (Online)
₩ 15000000 (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-29 10:00
21 hours
Seoul - Gran Seoul
₩ 7500000 (Online)
₩ 15000000 (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2027-01-12 10:00
21 hours
Seoul Center 1
₩ 7500000 (Online)
₩ 15000000 (Classroom)

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