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 Duration 21 hours

Course Outline

Foundations of Hybrid AI Deployment

  • Analyzing hybrid, cloud, and edge deployment architectures
  • Examining AI workload traits and infrastructure limitations
  • Selecting the most effective deployment topology

Containerizing AI Workloads with Docker

  • Creating inference containers for both GPU and CPU architectures
  • Overseeing secure image repositories and registries
  • Establishing reproducible environments for AI applications

Deploying AI Services in Cloud Environments

  • Executing inference on AWS, Azure, and GCP via Docker
  • Allocating cloud computing resources for model serving
  • Hardening cloud-based AI endpoints for security

Techniques for Edge and On-Premise Deployment

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

Hybrid Networking and Secure Connectivity

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

Orchestrating Distributed AI Deployments

  • Leveraging 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 various 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 caching strategies
  • Balancing computational loads between cloud and edge resources

Conclusion and Future Directions

Requirements

  • A solid grasp of containerization principles
  • Hands-on experience with Linux command-line interfaces
  • Knowledge of AI model deployment pipelines

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
ActiveOne Bucharest
743 EUR (Online)
1343 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-11-26 09:30
21 hours
Brasov, Calea Bucuresti Street
743 EUR (Online)
1343 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-10 09:30
21 hours
Sibiu
743 EUR (Online)
1343 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-24 09:30
21 hours
Cluj-Napoca, Pitești Street
743 EUR (Online)
1343 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2027-01-07 09:30
21 hours
Timisoara, Iosif Bulbuca Boulevard
743 EUR (Online)
1343 EUR (Classroom)

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