Course Outline

Foundations of Hybrid AI Deployment

  • Understanding hybrid, cloud, and edge deployment models
  • AI workload characteristics and infrastructure constraints
  • Choosing the right deployment topology

Containerizing AI Workloads with Docker

  • Building GPU and CPU inference containers
  • Managing secure images and registries
  • Implementing reproducible environments for AI

Deploying AI Services to Cloud Environments

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

Edge and On-Prem Deployment Techniques

  • Running AI on IoT devices, gateways, and microservers
  • Lightweight runtimes for edge environments
  • Managing intermittent connectivity and local persistence

Hybrid Networking and Secure Connectivity

  • Secure tunneling between edge and cloud
  • Certificates, secrets, and token-based access
  • Performance tuning for low-latency inference

Orchestrating Distributed AI Deployments

  • Using K3s, K8s, or lightweight orchestration for hybrid setups
  • Service discovery and workload scheduling
  • Automating multi-location rollout strategies

Monitoring and Observability Across Environments

  • Tracking inference performance across locations
  • Centralized logging for hybrid AI systems
  • Failure detection and automated recovery

Scaling and Optimizing Hybrid AI Systems

  • Scaling edge clusters and cloud nodes
  • Optimizing bandwidth usage and caching
  • Balancing compute loads between cloud and edge

Summary and Next Steps

Requirements

  • An understanding of containerization concepts
  • Experience with Linux command-line operations
  • Familiarity with AI model deployment workflows

Audience

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

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?

Testimonials (5)

OC is new to us and we learnt alot and the labs were excellent

sharkey dollie
Course - OpenShift 4 for Administrators

Very informative and to the point. Hands on pratice

Gil Matias - FINEOS
Course - Introduction to Docker

Labs and technical discussions.

Dinesh Panchal - AXA XL
Course - Advanced Docker

That Brian has good knowledge of the topic and explains it well

Francisco Demetrio Quitral - IMED S.A
Course - Rancher: administra tus contenedores Docker

Machine Translated

It gave a good grounding for Docker and Kubernetes.

Stephen Dowdeswell - Global Knowledge Networks UK
Course - Docker (introducing Kubernetes)

Upcoming Courses

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-02-09 09:30
21 hours
Malaga City Center

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-02-23 09:30
21 hours
Barcelona City

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-03-09 09:30
21 hours
Valencian Community

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-03-23 09:30
21 hours
Bilbao

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-04-06 09:30
21 hours
Madrid - Chamartín Business Area

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