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Duration 14 hours
Course Outline
Deployment Strategies
- Comparing hosted API versus self-hosted advantages and trade-offs.
- Patterns for on-premises, edge, and air-gapped environments.
- Single model versus managing a model portfolio.
- Comparative analysis of cost per decision.
Hardware Sizing and Capacity Planning
- Assessing memory footprint relative to parameter count.
- CPU-only execution versus GPU acceleration.
- Planning for concurrency and throughput.
- Allowing headroom for increasing task lengths.
Serving Stacks
- Evaluating vLLM and SGLang options.
- Utilising llama.cpp and GGUF execution.
- Using Ollama for local development workflows.
- Selecting tools based on the specific shape of the decision workload.
Quantisation While Preserving Calibration
- Precision formats and associated memory savings.
- Measuring quality degradation and calibration loss.
- Per-tensor and importance-aware quantisation variants.
- Conducting acceptance testing post-quantisation.
Latency Engineering
- Analysing median versus tail latency.
- Understanding latency growth as context length increases.
- Implementing warm starts and model caching.
- Batching multiple decision queries.
Edge and Constrained Environments
- Running small decision models on CPU resources.
- Deployment on Apple Silicon and embedded systems.
- Offline operation strategies and update protocols.
- Distributing weights in air-gapped scenarios.
Containerisation and Scaling
- Designing images optimized for model serving.
- Managing volumes and caches effectively.
- Implementing horizontal scaling and routing.
- Setting resource limits and sharing GPUs.
Observability
- Tracking request, latency, and error metrics.
- Monitoring confidence distributions.
- Tracking decision outcomes and escalations.
- Setting alerts for calibration drift.
Versioning and Rollout
- Versioning models and adapters.
- Deploying via shadow and canary methods.
- Using pinned model files for audit purposes.
- Establishing rollback procedures.
Security and Multi-Tenancy
- Implementing authentication and rate limiting.
- Ensuring tenant isolation for prompts and logs.
- Managing network isolation and egress controls.
- Maintaining audit logs for decisions.
Requirements
- Proficiency in Linux administration and container technologies.
- Practical experience with model inference servers and GPU resource management.
- A solid grasp of REST APIs, load balancing strategies, and system monitoring.
Target Audience
- MLOps and platform engineers managing inference infrastructure.
- SRE teams responsible for supporting AI services.
- AI engineers implementing on-premise decision model deployments.
- Architects designing secure, air-gapped, or regulatory-compliant AI solutions.