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Course Outline
Foundations of Hybrid AI Deployment
- Understanding hybrid, cloud, and edge deployment models
- Characteristics of AI workloads and infrastructure constraints
- Selecting the appropriate 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
- Executing inference on AWS, Azure, and GCP via Docker
- Provisioning cloud compute resources for model serving
- Securing cloud-based AI endpoints
Edge and On-Prem Deployment Techniques
- Running AI on IoT devices, gateways, and microservers
- Utilizing lightweight runtimes for edge environments
- Managing intermittent connectivity and local data persistence
Hybrid Networking and Secure Connectivity
- Establishing secure tunneling between edge and cloud
- Managing certificates, secrets, and token-based access
- Optimizing performance for low-latency inference
Orchestrating Distributed AI Deployments
- Utilizing 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
- Tracking inference performance across various locations
- Centralizing logs for hybrid AI systems
- Detecting failures and automating recovery processes
Scaling and Optimizing Hybrid AI Systems
- Scaling edge clusters and cloud nodes
- Optimizing bandwidth usage and caching mechanisms
- Balancing compute loads between cloud and edge resources
Summary and Next Steps
Requirements
- A solid 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