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Duration 21 hours
Course Outline
Foundations of Hybrid AI Deployment
- Exploring hybrid, cloud, and edge deployment models
- Analyzing AI workload characteristics and infrastructure limitations
- Selecting the optimal deployment topology
Containerizing AI Workloads with Docker
- Creating inference containers for GPU and CPU architectures
- Handling secure image management and registries
- Establishing reproducible environments for AI applications
Deploying AI Services to Cloud Environments
- Executing inference on AWS, Azure, and GCP using Docker
- Configuring cloud compute resources for model serving
- Protecting cloud-based AI endpoints
Edge and On-Premise Deployment Strategies
- Implementing AI on IoT devices, gateways, and microservers
- Utilizing lightweight runtimes for edge contexts
- Addressing intermittent connectivity and local data persistence
Hybrid Networking and Secure Connectivity
- Establishing secure tunnels between edge and cloud layers
- Managing certificates, secrets, and token-based access controls
- Tuning performance for low-latency inference
Orchestrating Distributed AI Deployments
- Applying K3s, K8s, or lightweight orchestration for hybrid setups
- Implementing service discovery and workload scheduling
- Automating rollout strategies across multiple locations
Monitoring and Observability Across Environments
- Monitoring inference performance across different sites
- Centralizing logs for hybrid AI systems
- Detecting failures and triggering automated recovery
Scaling and Optimizing Hybrid AI Systems
- Expanding edge clusters and cloud nodes
- Optimizing bandwidth consumption and caching strategies
- Balancing computational loads between cloud and edge resources
Summary and Next Steps
Requirements
- A solid grasp of containerization principles
- Proficiency with Linux command-line interfaces
- Knowledge of AI model deployment workflows
Target Audience
- Infrastructure architects
- Site Reliability Engineers (SREs)
- Edge and IoT developers
Testimonials (2)
multi-tiered, structured course programme.
Bert Paelinckx - Cube SoftwareSolutions
Course - Introduction to Docker
The training met expectations with its clear explanations, real-world examples, and hands-on labs that made complex topics easy to understand. It provided valuable insights into container orchestration, security, scaling and many other advanced topics.
