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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 optimal deployment topologies

Containerizing AI Workloads with Docker

  • Creating GPU and CPU inference containers
  • Administering secure images and registries
  • Establishing reproducible environments for AI

Deploying AI Services to Cloud Environments

  • Executing inference on AWS, Azure, and GCP via Docker
  • Provisioning cloud compute resources for model serving
  • Enhancing the security of cloud-based AI endpoints

Edge and On-Prem Deployment Techniques

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

Hybrid Networking and Secure Connectivity

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

Orchestrating Distributed AI Deployments

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

Monitoring and Observability Across Environments

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

Scaling and Optimizing Hybrid AI Systems

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

Conclusion and Future Steps

Requirements

  • Knowledge of containerization concepts
  • Proficiency with Linux command-line operations
  • Understanding of AI model deployment workflows

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

2026-11-16 09:30
21 hours
Bologna
3150 EUR (Online)
3750 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-11-30 09:30
21 hours
Rome
3150 EUR (Online)
3750 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-14 09:30
21 hours
Milan
3150 EUR (Online)
3750 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-28 09:30
21 hours
Naples
3150 EUR (Online)
3750 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2027-01-11 09:30
21 hours
Turin
3150 EUR (Online)
3750 EUR (Classroom)

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