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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 inference containers for both GPU and CPU architectures.
- Managing secure images and registries.
- Establishing reproducible environments for AI applications.
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-Premise Deployment Techniques
- Running AI models 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 nodes and the cloud.
- Handling certificates, secrets, and token-based access protocols.
- Tuning performance for low-latency inference scenarios.
Orchestrating Distributed AI Deployments
- Utilizing K3s, K8s, or lightweight orchestration tools for hybrid setups.
- Implementing service discovery and workload scheduling mechanisms.
- Automating rollout strategies across multiple locations.
Monitoring and Observability Across Environments
- Tracking inference performance metrics across different sites.
- Implementing centralized logging for hybrid AI systems.
- Detecting failures and enabling automated recovery processes.
Scaling and Optimizing Hybrid AI Systems
- Scaling edge clusters and cloud nodes effectively.
- Optimizing bandwidth usage and caching strategies.
- Balancing compute loads between cloud infrastructure and edge devices.
Summary and Next Steps
Requirements
- A solid understanding of containerization concepts.
- Experience performing operations via the Linux command-line interface.
- Familiarity with standard AI model deployment workflows.
Target Audience
- Infrastructure architects.
- Site Reliability Engineers (SREs).
- Developers specializing in Edge and IoT solutions.
21 Hours
Testimonials (3)
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
Bogdan Olaru
Course - Introduction to Docker
The knowledge and exchanges with Augustin
