The map
One graph. Every concept. A clear path.
Every concept is a node. Every edge is a prerequisite. Tracks run left to right across twelve weeks — follow an edge backward and you find exactly what to master first. No guessing what to learn next.
Concept map
Twelve weeks, mapped end to end.
Curved lines connect a concept to its prerequisites. The visual is decorative; the full, screen-reader-friendly version follows as a table.
Agentic Systems EngineeringAI Infrastructure & InferenceApplied ML & Model EngineeringProduction AI ProductsFrontier Systems
Every concept, in order
The graph as a table.
A complete, accessible listing of every node, the classic CS subject it bridges to, and its prerequisite edges.
| Week | Track | Concept | Bridges to | Prerequisites |
|---|---|---|---|---|
| 1 | Agentic Systems Engineering | The Augmented LLM as a Building Block | Operating Systems — processes, scheduling, and the run loop | None |
| 1 | AI Infrastructure & Inference | Transformer Internals for Serving | Computer Architecture — instruction-level parallelism and the memory wall | None |
| 1 | Applied ML & Model Engineering | Neural Networks & Backpropagation from Scratch | Calculus & Linear Algebra — gradients, the chain rule, and vector spaces | None |
| 1 | Frontier Systems | Distributed Systems Foundations | Distributed Systems — failure models, latency, and the CAP theorem | None |
| 1 | Production AI Products | LLM Application Foundations | Software Engineering — application architecture and API design | None |
| 2 | Agentic Systems Engineering | Tool Use & Function Calling | Software Engineering — interface design and API contracts | The Augmented LLM as a Building Block |
| 2 | AI Infrastructure & Inference | GPU Architecture & the Memory Wall | Computer Architecture — parallelism, memory hierarchy, and the roofline model | Transformer Internals for Serving |
| 2 | Applied ML & Model Engineering | Deep Learning Foundations | Machine Learning — optimization, generalization, and the bias-variance tradeoff | Neural Networks & Backpropagation from Scratch |
| 2 | Production AI Products | Embeddings & Vector Search | Databases — indexing, search structures, and query optimization | LLM Application Foundations |
| 3 | Agentic Systems Engineering | Model Context Protocol & Interoperable Tooling | Computer Networks — protocols, client-server architecture, and RPC | Tool Use & Function Calling |
| 3 | AI Infrastructure & Inference | FlashAttention & Kernel-Level Optimization | Operating Systems — I/O scheduling and memory-bound versus compute-bound work | GPU Architecture & the Memory Wall |
| 3 | Applied ML & Model Engineering | Transformers, Attention & Pretraining | Machine Learning — sequence modeling and representation learning | Deep Learning Foundations |
| 3 | Frontier Systems | Consensus & Coordination | Distributed Systems — consensus, replication, and fault tolerance | Distributed Systems Foundations |
| 4 | Agentic Systems Engineering | Planning, Reasoning & Task Decomposition | Artificial Intelligence — search, planning, and state-space reasoning | Tool Use & Function Calling |
| 4 | AI Infrastructure & Inference | KV-Cache & Paged Attention | Operating Systems — virtual memory, paging, and fragmentation | FlashAttention & Kernel-Level Optimization |
| 4 | Applied ML & Model Engineering | Training Data Engineering | Databases — data cleaning, deduplication, and ETL pipelines | Transformers, Attention & Pretraining |
| 4 | Frontier Systems | Distributed Training & Parallelism | Parallel Computing — collective communication and parallel decomposition | Consensus & Coordination |
| 4 | Production AI Products | Production RAG & Context Engineering | Databases — query processing, joins, and information retrieval | Embeddings & Vector Search |
| 5 | Agentic Systems Engineering | Agent Memory & Context Management | Operating Systems — memory hierarchy, paging, and caching | Planning, Reasoning & Task Decomposition |
| 5 | AI Infrastructure & Inference | Continuous Batching & Throughput Scheduling | Operating Systems — CPU scheduling, throughput versus latency tradeoffs | KV-Cache & Paged Attention |
| 5 | Production AI Products | LLM Evaluation Harnesses | Software Engineering — automated testing and continuous integration | Production RAG & Context Engineering |
| 6 | Agentic Systems Engineering | Sandboxed Code Execution | Operating Systems — virtualization, namespaces, and process isolation | Agent Memory & Context Management |
| 6 | Applied ML & Model Engineering | Supervised Fine-Tuning | Machine Learning — transfer learning and supervised training | Training Data Engineering |
| 6 | Frontier Systems | GPU Cluster Scheduling | Operating Systems — scheduling, resource allocation, and fairness | Distributed Training & Parallelism |
| 7 | AI Infrastructure & Inference | Quantization & Model Compression | Computer Architecture — number representation and fixed-point arithmetic | Continuous Batching & Throughput Scheduling |
| 7 | Applied ML & Model Engineering | Parameter-Efficient Fine-Tuning: LoRA & QLoRA | Linear Algebra — matrix rank, decomposition, and low-rank approximation | Supervised Fine-Tuning |
| 7 | Frontier Systems | Real-Time & Streaming AI Systems | Distributed Systems — event-driven architecture and stream processing | GPU Cluster Scheduling |
| 7 | Production AI Products | AI Observability & Tracing | Software Engineering — monitoring, logging, and distributed tracing | LLM Evaluation Harnesses |
| 8 | Agentic Systems Engineering | Multi-Agent Orchestration | Distributed Systems — coordination, message passing, and consensus | Sandboxed Code Execution |
| 8 | AI Infrastructure & Inference | Speculative Decoding & Latency Optimization | Computer Architecture — speculative and out-of-order execution | Quantization & Model Compression |
| 9 | Applied ML & Model Engineering | Preference Optimization: RLHF & DPO | Machine Learning — reinforcement learning and policy optimization | Parameter-Efficient Fine-Tuning: LoRA & QLoRA |
| 9 | Frontier Systems | Vector Databases at Scale | Databases — indexing, sharding, and query optimization | Real-Time & Streaming AI Systems |
| 9 | Production AI Products | AI Security & Red-Teaming | Information Security — threat modeling, penetration testing, and OWASP | AI Observability & Tracing |
| 10 | Agentic Systems Engineering | Evaluating & Observing Agents | Software Engineering — testing, regression suites, and observability | Multi-Agent Orchestration |
| 10 | AI Infrastructure & Inference | Production Serving & Autoscaling | Distributed Systems — load balancing, replication, and capacity planning | Speculative Decoding & Latency Optimization |
| 10 | Applied ML & Model Engineering | Knowledge Distillation & Model Compression | Machine Learning — model compression and the teacher-student paradigm | Preference Optimization: RLHF & DPO |
| 10 | Production AI Products | LLMOps & Cost Governance | Software Engineering — release management, versioning, and CI/CD | AI Security & Red-Teaming |
| 11 | Agentic Systems Engineering | Agent Security & Prompt Injection | Information Security — threat modeling and least privilege | Evaluating & Observing Agents |
| 11 | Applied ML & Model Engineering | Rigorous Model Evaluation | Statistics — sampling, confidence intervals, and experimental design | Knowledge Distillation & Model Compression |
| 11 | Frontier Systems | Fault Tolerance & Resilient Operations | Distributed Systems — fault tolerance, checkpointing, and recovery | Vector Databases at Scale |
| 12 | Frontier Systems | Cluster Observability & Capacity Planning | Distributed Systems — monitoring, capacity planning, and performance analysis | Fault Tolerance & Resilient Operations |
| 12 | Production AI Products | Shipping & Operating AI Products | Software Engineering — deployment, resilience, and product engineering | LLMOps & Cost Governance |