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Duration 21 hours
Course Outline
Foundations of Hybrid AI Deployment
- Analyzing hybrid, cloud-native, and edge deployment architectures
- Evaluating AI workload requirements against infrastructure limitations
- Selecting the optimal deployment topology
Containerizing AI Workloads with Docker
- Creating inference containers for both GPU and CPU resources
- Oversight of secure image storage and registry management
- Establishing consistent, reproducible environments for AI operations
Deploying AI Services to Cloud Environments
- Executing inference on AWS, Azure, and GCP platforms via Docker
- Allocating cloud compute resources for model serving
- Hardening cloud-based AI endpoints for security
Edge and On-Prem Deployment Techniques
- Executing AI models on IoT devices, gateways, and microservers
- Utilizing lightweight runtimes optimized for edge contexts
- Handling intermittent connectivity and managing local data persistence
Hybrid Networking and Secure Connectivity
- Establishing secure tunnels between edge nodes and the cloud
- Managing certificates, secrets, and token-based authentication
- Tuning performance metrics for minimal inference latency
Orchestrating Distributed AI Deployments
- Leveraging K3s, K8s, or lightweight orchestration for hybrid architectures
- Implementing service discovery and intelligent workload scheduling
- Automating rollout strategies across multiple locations
Monitoring and Observability Across Environments
- Tracking inference performance metrics across various sites
- Implementing centralized logging for hybrid AI systems
- Detecting failures and triggering automated recovery mechanisms
Scaling and Optimizing Hybrid AI Systems
- Scaling edge clusters and expanding cloud node capacity
- Optimizing bandwidth consumption and implementing caching strategies
- Balancing computational loads between cloud and edge resources
Summary and Next Steps
Requirements
- A solid grasp of containerization principles
- Practical experience with Linux command-line interfaces
- Knowledge of standard AI model deployment processes
Target Audience
- Infrastructure architects
- Site Reliability Engineers (SREs)
- Edge and IoT developers
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.
