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Course Outline
Foundations of Hybrid AI Deployment
- Exploring hybrid, cloud, and edge deployment models.
- Analyzing AI workload characteristics and infrastructure constraints.
- Selecting the appropriate deployment topology.
Containerizing AI Workloads with Docker
- Constructing GPU and CPU inference containers.
- Managing secure images and registries.
- Establishing reproducible environments for AI development.
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-Premises Deployment Techniques
- Running AI applications 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 tunnels between edge nodes and the cloud.
- Managing certificates, secrets, and token-based access.
- Tuning performance for low-latency inference.
Orchestrating Distributed AI Deployments
- Leveraging K3s, K8s, or lightweight orchestration tools 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.
- 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 resources.
Summary and Next Steps
Requirements
- Familiarity with containerization concepts.
- Experience with Linux command-line operations.
- Understanding of AI model deployment workflows.
Audience
- Infrastructure architects.
- Site Reliability Engineers (SREs).
- Edge and IoT developers.
21 Hours
Testimonials (1)
The training met expectations with its clear explanations, real-world examples, and hands-on labs that made complex topics easy to understand. It provided valuable insights into container orchestration, security, scaling and many other advanced topics.
