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 Duration 21 hours

Course Outline

Foundations of Hybrid AI Deployment

  • Exploring hybrid, cloud, and edge deployment models
  • Characteristics of AI workloads and related infrastructure constraints
  • Selecting the optimal deployment topology

Containerizing AI Workloads with Docker

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

Deploying AI Services to Cloud Environments

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

Edge and On-Prem Deployment Techniques

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

Hybrid Networking and Secure Connectivity

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

Orchestrating Distributed AI Deployments

  • Utilizing K3s, K8s, or lightweight orchestration tools 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 executing automated recovery procedures

Scaling and Optimizing Hybrid AI Systems

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

Summary and Next Steps

Requirements

  • A solid grasp of containerization concepts
  • Practical experience with Linux command-line operations
  • Acquaintance 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

Upcoming Courses

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-11-12 09:30
21 hours
Fortaleza - Meireles
$ 8730 (Online)
$ 10230 (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-11-26 09:30
21 hours
Fortaleza - Etevaldo Nogueira Building
$ 8730 (Online)
$ 10230 (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-10 09:30
21 hours
Curitiba - Corporate Evolution
$ 8730 (Online)
$ 10230 (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-24 09:30
21 hours
Belo Horizonte - Amadeus Business Tower
$ 8730 (Online)
$ 10230 (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2027-01-07 09:30
21 hours
Porto Alegre - Platinum Building
$ 8730 (Online)
$ 10230 (Classroom)

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