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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

  • Running inference on AWS, Azure, and GCP via Docker.
  • Provisioning cloud compute resources for model serving.
  • Securing cloud-based AI endpoints.

Edge and On-Premises Deployment Techniques

  • Running AI on IoT devices, gateways, and microservers.
  • Utilizing lightweight runtimes for edge environments.
  • Managing intermittent connectivity and local persistence.

Hybrid Networking and Secure Connectivity

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

Orchestrating Distributed AI Deployments

  • Using K3s, K8s, or lightweight orchestration for hybrid setups.
  • Implementing service discovery and workload scheduling.
  • Automating multi-location rollout strategies.

Monitoring and Observability Across Environments

  • Tracking inference performance across various locations.
  • Implementing centralized logging for hybrid AI systems.
  • Detecting failures and enabling automated recovery.

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.

Summary and Next Steps

Requirements

  • A solid understanding of containerization concepts.
  • Practical experience with Linux command-line operations.
  • Familiarity with AI model deployment workflows.

Target Audience

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

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