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 Duration 21 hours

Course Outline

Foundations of Hybrid AI Deployment

  • Exploring hybrid, cloud, and edge deployment models
  • Analyzing AI workload traits and infrastructure limitations
  • Selecting optimal deployment topologies

Containerizing AI Workloads with Docker

  • Creating GPU and CPU inference containers
  • Overseeing secure images and registries
  • Establishing reproducible environments for AI applications

Implementing AI Services in Cloud Environments

  • Executing inference on AWS, Azure, and GCP using Docker
  • Allocating cloud compute resources for model serving
  • Protecting cloud-based AI endpoints

Edge and On-Prem Deployment Strategies

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

Hybrid Networking and Secure Connections

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

Orchestrating Distributed AI Deployments

  • Leveraging K3s, K8s, or lightweight orchestration for hybrid configurations
  • Managing service discovery and workload scheduling
  • Automating rollout strategies across multiple locations

Monitoring and Observability Across Environments

  • Monitoring inference performance across different sites
  • Implementing centralized logging for hybrid AI systems
  • Detecting failures and triggering automated recovery

Scaling and Optimizing Hybrid AI Systems

  • Scaling edge clusters and cloud nodes
  • Optimizing bandwidth consumption and caching mechanisms
  • Balancing compute loads between cloud and edge resources

Summary and Future Steps

Requirements

  • A solid grasp of containerization principles
  • Proficiency in Linux command-line operations
  • Knowledge of AI model deployment workflows

Target Audience

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

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