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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

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