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 Duration 21 hours

Course Outline

Core Principles of Hybrid AI Deployment

  • Analyzing hybrid, cloud, and edge deployment architectures
  • Evaluating AI workload traits and underlying infrastructure limitations
  • Selecting the optimal deployment topology for specific needs

Encapsulating AI Workloads with Docker

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

Deploying AI Services to Cloud Platforms

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

Techniques for Edge and On-Premise Deployment

  • Executing AI workloads on IoT devices, gateways, and microservers
  • Utilizing lightweight runtimes suitable for edge environments
  • Handling intermittent connectivity and managing local data persistence

Hybrid Networking and Secure Connectivity

  • Establishing secure tunnels between edge and cloud layers
  • Managing certificates, secrets, and token-based access controls
  • Tuning performance for low-latency inference requirements

Orchestrating Distributed AI Deployments

  • Utilizing K3s, K8s, or lightweight orchestration tools for hybrid configurations
  • Implementing service discovery and intelligent workload scheduling
  • Automating rollout strategies across multiple locations

Monitoring and Observability Across Environments

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

Scaling and Optimizing Hybrid AI Systems

  • Expanding edge clusters and cloud node capacity
  • Optimizing bandwidth consumption and data caching strategies
  • Balancing compute loads effectively between cloud and edge nodes

Conclusion and Future Directions

Requirements

  • A solid grasp of containerization principles
  • Proficiency in Linux command-line operations
  • Knowledge of 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 09:30
21 hours
Taichung, Global
29700 NT$ (Online)
218700 NT$ (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-01 09:30
21 hours
The Hsinchu Wishun centre
29700 NT$ (Online)
29700 NT$ (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-15 09:30
21 hours
Taipei, Aurora
29700 NT$ (Online)
29700 NT$ (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-29 09:30
21 hours
Taipei- Concord
29700 NT$ (Online)
29700 NT$ (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2027-01-12 09:30
21 hours
Taipei Walsin Xinyi
29700 NT$ (Online)
29700 NT$ (Classroom)

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