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Course Outline

Foundations of Hybrid AI Deployment

  • Comprehending hybrid, cloud, and edge deployment models
  • Analyzing AI workload characteristics and infrastructure limitations
  • Selecting the appropriate deployment topology

Containerizing AI Workloads with Docker

  • Creating GPU and CPU inference containers
  • Managing 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
  • Securing cloud-based AI endpoints

Edge and On-Premise Deployment Techniques

  • Running AI on IoT devices, gateways, and microservers
  • Utilizing lightweight runtimes for edge settings
  • Handling intermittent connectivity and local data persistence

Hybrid Networking and Secure Connectivity

  • Establishing secure tunnels between edge nodes and the cloud
  • Managing certificates, secrets, and token-based access control
  • Tuning performance for low-latency inference

Orchestrating Distributed AI Deployments

  • Leveraging K3s, K8s, or lightweight orchestration for hybrid configurations
  • Facilitating service discovery and 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 enabling automated recovery mechanisms

Scaling and Optimizing Hybrid AI Systems

  • Expanding edge clusters and cloud nodes
  • Optimizing bandwidth utilization and caching strategies
  • Balancing computational loads between cloud and edge environments

Summary and Next Steps

Requirements

  • Familiarity with containerization principles
  • Experience using the Linux command line
  • Understanding of AI model deployment workflows

Target Audience

  • Infrastructure architects
  • Site Reliability Engineers (SREs)
  • Edge and IoT developers
 21 Hours

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