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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.

Containerising AI Workloads with Docker

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

Deploying AI Services to Cloud Environments

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

Edge and On-Prem Deployment Techniques

  • Executing AI on IoT devices, gateways, and microservers.
  • Utilising lightweight runtimes for edge environments.
  • Handling intermittent connectivity and local data 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

  • Leveraging 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 various locations.
  • Centralising logging for hybrid AI systems.
  • Detecting failures and enabling automated recovery.

Scaling and Optimizing Hybrid AI Systems

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

Summary and Next Steps

Requirements

  • A foundational understanding of containerisation concepts.
  • Practical experience with Linux command-line operations.
  • Familiarity with 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

Provisional Upcoming Courses (Require 5+ participants)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-11-30 09:30
21 hours
Melbourne 385 Bourke Street
7050 AUD (Online)
16950 AUD (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-14 09:30
21 hours
Cliftons Perth
7050 AUD (Online)
17850 AUD (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-28 09:30
21 hours
Sydney King Street
7050 AUD (Online)
16950 AUD (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2027-01-11 09:30
21 hours
London Circuit
7050 AUD (Online)
16950 AUD (Classroom)

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