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 Duration 21 hours

Course Outline

Core Principles of Hybrid AI Deployment

  • Analyzing hybrid, cloud, and edge deployment architectures
  • Assessing AI workload traits and infrastructure limitations
  • Selecting the optimal deployment topology

Containerizing AI Workloads via Docker

  • Creating inference containers for GPU and CPU platforms
  • Overseeing secure images and registry management
  • Establishing reproducible environments for AI applications

Implementing AI Services in Cloud Platforms

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

Techniques for Edge and On-Prem Implementation

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

Hybrid Networking and Secure Connectivity

  • Establishing secure tunnels between edge and cloud components
  • Managing certificates, secrets, and token-based authorization
  • Tuning performance for low-latency inference

Orchestrating Distributed AI Architectures

  • Employing K3s, K8s, or lightweight orchestration for hybrid configurations
  • Managing service discovery and workload scheduling
  • Automating rollout strategies across multiple locations

Cross-Environment Monitoring and Observability

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

Scaling and Optimizing Hybrid AI Systems

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

Conclusion and Future Directions

Requirements

  • A foundational grasp of containerization principles
  • Practical experience with Linux command-line utilities
  • 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

2026-11-25 09:30
21 hours
Green Heart Complex
506 EUR (Online)
506 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-09 09:30
21 hours
Green Heart Complex
506 EUR (Online)
506 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-23 09:30
21 hours
Green Heart Complex
506 EUR (Online)
506 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2027-01-06 09:30
21 hours
Green Heart Complex
506 EUR (Online)
506 EUR (Classroom)

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