30 notes · CC BY 4.0
Systems Engineering
The research behind course 002: requirements, architecture, trade studies, V&V, lifecycle standards.
Working notes from the research behind the course: mechanisms, tables, failure modes and sources. They are written for an engineer, not a beginner — the course is the beginner-facing version of the same material.
| Note | Length |
|---|---|
| Overview | |
| Systems Engineering — The Sauce Systems Engineering is the discipline of designing, analyzing, and managing complex systems over their life cycles. It sits at the intersection of… | 796 words |
| AI-Systems-Engineering | |
| AI-Systems-Engineering This section bridges traditional Systems Engineering with the AI infrastructure content already in the wiki. Treat AI systems (models, agents,… | 360 words |
| Case-Studies | |
| Case Studies: Software-Intensive Systems Bridging Systems Engineering with Software and Agentic Engineering through practical examples. | 731 words |
| EdgeAI | |
| Local Inference Runtimes On-device and edge inference runtimes: llama.cpp, MLX, ONNX Runtime, Core ML, TensorFlow Lite — when to use each and how to deploy. | 637 words |
| Quantization for Edge Quantization techniques for deploying AI models on edge and on-device hardware: INT8, INT4, AWQ, GPTQ, GGUF, and runtime selection. | 499 words |
| EdgeAI On-device AI: quantization, small language models, local inference, and edge deployment. | 285 words |
| Foundations | |
| Complexity and Emergence Not all problems are equally hard. Understanding the difference between complicated and complex systems is essential for choosing the right approach. | 788 words |
| Systems Engineering History and Standards Understanding the history and standards of Systems Engineering helps put modern AI systems engineering in context. | 914 words |
| Systems Thinking Systems thinking is the cognitive foundation of Systems Engineering. It means looking at wholes rather than just parts, understanding… | 906 words |
| Inference | |
| Batching and Quantization Continuous batching, PagedAttention, and quantization — the three levers that determine inference throughput, latency, and cost per token. | 815 words |
| Inference Engines Model serving infrastructure for AI systems: vLLM, SGLang, TensorRT-LLM, NVIDIA Triton, and Hugging Face TGI — how they work, when to choose each,… | 695 words |
| Inference Model serving and inference for AI systems: inference engines, serving stacks, batching, optimization, and cost-per-token governance. | 353 words |
| Lifecycle | |
| INCOSE/ISO 15288 Life Cycle Processes ISO/IEC/IEEE 15288 defines a comprehensive set of processes for systems throughout their life cycle. This is the backbone of modern Systems… | 931 words |
| MLOps | |
| Canary and Rollback Progressive delivery for AI models: canary deployments, automated rollback, eval gates, and safe rollout patterns. | 718 words |
| Model Registry and Lineage Versioned model store with metadata, lineage, and reproducibility for AI systems: tracking data, code, config, and deployment history per model… | 497 words |
| MLOps Deployment, training pipelines, LLMOps, rollout, and lifecycle management for AI systems. | 324 words |
| Observability | |
| Eval in Production Continuous evaluation for AI systems: LLM-as-judge, regression sets, faithfulness checks, and drift detection on live traffic. | 678 words |
| Observability Stack A complete observability architecture for AI systems: metrics, tracing, logging, alerting, and continuous evaluation across model, retrieval, and… | 515 words |
| Observability Monitoring, tracing, logging, alerting, and continuous evaluation for AI systems in production. | 281 words |
| Platform | |
| Internal Developer Platform Developer experience, self-service, and internal tooling for AI platform teams: golden paths, model serving as a service, prompt management, and… | 542 words |
| Prompt Management and Cost Showback Central prompt registry with versioning, review, and AI cost attribution by team and product. | 1051 words |
| Platform Developer experience, self-service, and internal tooling for AI platform teams. | 292 words |
| Reliability | |
| Circuit Breakers and Fallbacks Reliability patterns for AI systems: circuit breakers, fallback models, retry budgets, and graceful degradation when models fail or degrade. | 829 words |
| Guardrails and Safety Input and output guardrails, failover patterns, and incident response for AI systems operating in production. | 600 words |
| Reliability Guardrails, failover, safety, and incident response for AI systems in production. | 296 words |
| Requirements-Engineering | |
| Needs vs Requirements One of the most important distinctions in Systems Engineering: needs describe what stakeholders want to achieve, requirements describe what the… | 709 words |
| Writing Good Requirements Good requirements are clear, unambiguous, verifiable, and traceable. This guide covers characteristics, patterns, and common mistakes. | 874 words |
| Risk-Reliability | |
| Resilience Engineering Resilience is the ability of a system to adapt and recover in the face of disturbances, unexpected conditions, or failures. For complex systems,… | 956 words |
| Trade-Studies | |
| Trade Study Methodology A trade study is a structured, objective comparison of alternatives to support a decision. The goal isn't to "prove" your favorite choice - it's… | 996 words |
| Verification-Validation | |
| V-Model and V&V Planning Verification and Validation (V&V) ensure we build the right system correctly. The V-Model is a classic SE approach that pairs development… | 989 words |