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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
Testimonials (2)
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
the trainer had a lot of knowledge and patience to share with us