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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.

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