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Duration 14 hours
Course Outline
Deployment Strategies
- Evaluating trade-offs between Hosted APIs and self-hosted solutions.
- Patterns for on-premises, edge, and air-gapped environments.
- Considerations for single models versus model portfolios.
- Comparing cost-per-decision metrics.
Hardware Sizing and Capacity Planning
- Assessing memory footprint relative to parameter counts.
- Comparing CPU-only versus GPU-accelerated performance.
- Planning for concurrency and throughput.
- Accounting for growth in task length.
Serving Stacks
- Exploring vLLM and SGLang options.
- Utilizing llama.cpp and GGUF execution.
- Using Ollama for local development workflows.
- Selecting stacks based on the shape of the decision workload.
Quantization While Preserving Calibration
- Understanding precision formats and associated memory savings.
- Measuring quality and calibration loss.
- Implementing per-tensor and importance-aware quantization variants.
- Conducting acceptance testing post-quantization.
Latency Optimization
- Analyzing median versus tail latency.
- Tracking latency growth in relation to context length.
- Leveraging warm starts and model caching.
- Batching multiple decision queries.
Edge and Constrained Environments
- Running small decision models on CPU.
- Deployment on Apple Silicon and embedded systems.
- Strategies for offline operation and updates.
- Distributing weights in air-gapped scenarios.
Containerization and Scaling
- Designing images for model serving.
- Managing volumes and caches.
- Implementing horizontal scaling and routing.
- Configuring resource limits and GPU sharing.
Observability
- Tracking request, latency, and error metrics.
- Monitoring confidence distributions.
- Logging decision outcomes and escalations.
- Setting alerts for calibration drift.
Versioning and Deployment
- Managing model and adapter versions.
- Executing shadow and canary deployments.
- Maintaining pinned model files for audit purposes.
- Defining rollback procedures.
Security and Multi-Tenancy
- Implementing authentication and rate limiting.
- Ensuring tenant isolation for prompts and logs.
- Configuring network isolation and egress controls.
- Maintaining audit logs of all decisions.
Requirements
- Proficiency in Linux administration and containerization.
- Experience with model inference servers and GPU management.
- A foundational 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 solutions.