Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Deployment Options
- Trade-offs between Hosted APIs and self-hosted solutions
- Patterns for on-premises, edge, and air-gapped environments
- Single model versus model portfolio strategies
- Comparing cost per decision
Hardware Sizing and Capacity
- Memory footprint based on parameter count
- CPU-only versus GPU acceleration
- Planning for concurrency and throughput
- Accounting for growth in task length
Serving Stacks
- vLLM and SGLang options
- llama.cpp and GGUF execution
- Ollama for local development
- Selecting based on the shape of the decision workload
Quantisation Without Losing Calibration
- Precision formats and memory savings
- Measuring quality and calibration loss
- Per-tensor and importance-aware variants
- Acceptance testing post-quantisation
Latency Engineering
- Median versus tail latency
- Latency impact of context length
- Warm start and model caching techniques
- Batching multiple decision queries
Edge and Constrained Environments
- Running small decision models on CPU
- Deployment on Apple Silicon and embedded devices
- Offline operation and update strategies
- Distributing weights in air-gapped settings
Containerisation and Scaling
- Designing images for model serving
- Managing volumes and caches
- Horizontal scaling and routing
- Resource limits and GPU sharing
Observability
- Monitoring request, latency, and error metrics
- Tracking confidence distributions
- Monitoring decision outcomes and escalations
- Alerting on calibration drift
Versioning and Rollout
- Versioning models and adapters
- Shadow and canary deployment strategies
- Pinning model files for audit purposes
- Rollback procedures
Security and Multi-Tenancy
- Authentication and rate limiting
- Tenant isolation for prompts and logs
- Network isolation and egress control
- Auditing decision logs
Requirements
- Experience with Linux administration and containers.
- Familiarity with model inference servers and GPU management.
- Basic understanding of REST APIs, load balancing, and monitoring.
Target Audience
- MLOps and platform engineers managing inference infrastructure
- SRE teams supporting AI services
- AI engineers deploying decision models on-premises
- Architects designing air-gapped or regulated AI deployments
