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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-Prem 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.
- Performance tuning for low-latency inference.
Orchestrating Distributed AI Deployments
- Using K3s, K8s, or lightweight orchestration for hybrid setups.
- Facilitating 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.
- Enabling failure detection and automated recovery.
Scaling and Optimizing Hybrid AI Systems
- Scaling edge clusters and cloud nodes.
- Optimizing bandwidth usage and caching strategies.
- Balancing compute loads between cloud and edge.
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
Testimonials (2)
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
the trainer had a lot of knowledge and patience to share with us