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Duration 14 hours
Course Outline
Deployment Options
- Hosted API versus self-hosted trade-offs
- On-premises, edge and air-gapped patterns
- Single model versus model portfolio
- Cost per decision comparison
Hardware Sizing and Capacity
- Memory footprint by parameter count
- CPU-only versus GPU acceleration
- Concurrency and throughput planning
- Headroom for task length growth
Serving Stacks
- vLLM and SGLang options
- llama.cpp and GGUF execution
- Ollama for local development
- Choosing based on decision workload shape
Quantisation Without Losing Calibration
- Precision formats and memory savings
- Measuring quality and calibration loss
- Per-tensor and importance-aware variants
- Acceptance testing after quantisation
Latency Engineering
- Median versus tail latency
- Latency growth with context length
- Warm start and model caching
- Batching multiple decision questions
Edge and Constrained Environments
- Small decision models on CPU
- Apple Silicon and embedded deployment
- Offline operation and update strategy
- Air-gapped weight distribution
Containerisation and Scaling
- Image design for model serving
- Volume and cache management
- Horizontal scaling and routing
- Resource limits and GPU sharing
Observability
- Request, latency and error metrics
- Confidence distribution monitoring
- Decision outcome and escalation tracking
- Alerting on calibration drift
Versioning and Rollout
- Model and adapter versioning
- Shadow and canary deployment
- Pinned model files for audit
- Rollback procedures
Security and Multi-Tenancy
- Authentication and rate limiting
- Tenant isolation for prompts and logs
- Network isolation and egress control
- Audit logging of decisions
Requirements
- Linux administration and container experience.
- Familiarity with model inference servers and GPU management.
- Basic understanding of REST APIs, load balancing and monitoring.
Audience
- MLOps and platform engineers running inference infrastructure
- SRE teams supporting AI services
- AI engineers deploying decision models on premises
- Architects designing air-gapped or regulated AI deployments
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customised Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from £3200 + VAT*
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