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
Containerizing AI Workloads with Docker
- Creating GPU and CPU inference containers
- Administering 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
- Hardening cloud-based AI endpoints for security
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 systems
- Implementing certificates, secrets, and token-based access controls
- 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
- Monitoring inference performance across various locations
- Centralizing logs for hybrid AI systems
- Detecting failures and enabling 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 with Linux command-line operations
- Knowledge of AI model deployment workflows
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.
Price per private group, online live training, starting from 3900 € + VAT*
Contact us for an exact quote and to hear our latest promotions
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.
