Course Outline
Foundations of Hybrid AI Deployment
- Understanding hybrid, cloud, and edge deployment models
- AI workload characteristics and infrastructure constraints
- Choosing the right 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 for model serving
- Securing cloud-based AI endpoints
Edge and On-Prem Deployment Techniques
- Running AI on IoT devices, gateways, and microservers
- Lightweight runtimes for edge environments
- Managing intermittent connectivity and local persistence
Hybrid Networking and Secure Connectivity
- Secure tunneling between edge and cloud
- 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
- Service discovery and workload scheduling
- Automating multi-location rollout strategies
Monitoring and Observability Across Environments
- Tracking inference performance across locations
- Centralized logging for hybrid AI systems
- Failure detection and automated recovery
Scaling and Optimizing Hybrid AI Systems
- Scaling edge clusters and cloud nodes
- Optimizing bandwidth usage and caching
- Balancing compute loads between cloud and edge
Summary and Next Steps
Requirements
- An 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
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from £5700 online delivery, based on a group of 2 delegates, £1800 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
Please see our public courses
Testimonials (5)
OC is new to us and we learnt alot and the labs were excellent
sharkey dollie
Course - OpenShift 4 for Administrators
Very informative and to the point. Hands on pratice
Gil Matias - FINEOS
Course - Introduction to Docker
Labs and technical discussions.
Dinesh Panchal - AXA XL
Course - Advanced Docker
It gave a good grounding for Docker and Kubernetes.
Stephen Dowdeswell - Global Knowledge Networks UK
Course - Docker (introducing Kubernetes)
I mostly enjoyed the knowledge of the trainer.