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 Duration 21 hours

Course Outline

Foundations of Hybrid AI Deployment

  • Exploring hybrid, cloud, and edge deployment models
  • Assessing AI workload characteristics and infrastructure limitations
  • Selecting the optimal deployment topology

Containerizing AI Workloads with Docker

  • Developing GPU and CPU-based inference containers
  • Managing secure images and container registries
  • Establishing reproducible AI environments

Deploying AI Services to Cloud Environments

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

Edge and On-Premise Deployment Strategies

  • Running 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 nodes
  • Implementing certificates, secrets, and token-based access control
  • Tuning performance for low-latency inference

Orchestrating Distributed AI Deployments

  • Employing 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 various sites
  • Implementing centralized logging for hybrid AI systems
  • Detecting failures and triggering automated recovery

Scaling and Optimizing Hybrid AI Systems

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

Summary and Next Steps

Requirements

  • A solid grasp of containerization principles
  • Proficiency in Linux command-line operations
  • Working knowledge of AI model deployment pipelines

Target Audience

  • Infrastructure architects
  • Site Reliability Engineers (SREs)
  • Edge and IoT developers

Custom Corporate Training

Training solutions designed exclusively for businesses.

  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
Investment

Price per private group, online live training, starting from 4800 € + VAT*

Contact us for an exact quote and to hear our latest promotions

(*The final price may vary depending on the technical specialization of the course, the level of customization, the method of delivery and the number of learners)

Need help picking the right course?
opleidingen@nobleprog.com or +31 208 080 666

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 (Contact Us For More Information)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-11-30 09:30
21 hours
Enschede

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-14 09:30
21 hours
Almere

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-28 09:30
21 hours
Delft

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2027-01-11 09:30
21 hours
Amersfoort

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