# ai-infra-curriculum/ai-infra-engineer-learning architecture > Architecture diagram of the GitHub repository ai-infra-curriculum/ai-infra-engineer-learning, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-27). It is an AI-made overview: check details against the source. - Interactive diagram: https://gitdiagram.com/ai-infra-curriculum/ai-infra-engineer-learning - Repository: https://github.com/ai-infra-curriculum/ai-infra-engineer-learning ## Overview 1. **What this repository is** - This is not a runtime application repo; it is an **education platform repository** for an AI Infrastructure Engineer learning path. - Its architecture is centered on **content delivery**: lessons, labs, quizzes, assessments, and three capstone-style projects. 2. **Top-level structure** - The repo splits into four main subsystems: - `lessons/` for module-based curriculum content - `projects/` for production-style hands-on systems - `assessments/` for quizzes, exams, rubrics, and answer keys - `resources/` for cheat sheets, FAQs, and reading lists - Supporting root files (`README.md`, `CURRICULUM.md`, `GETTING_STARTED.md`, `VERSIONS.md`, `Makefile`, `requirements.txt`, `pyproject.toml`) provide navigation, standards, and repo-wide tooling. 3. **Curriculum content subsystem** - `lessons/mod-101` through `lessons/mod-110` form the core learning spine. - Each module is organized the same way: lecture docs, lab docs, exercises, quizzes, and a module README. - The content flows from fundamentals to advanced systems: foundations → cloud → containers → Kubernetes → pipelines → MLOps → GPU computing → observability → IaC → LLM infrastructure. 4. **Learning progression boundary** - The lesson modules are **static instructional content**, not executable product code. - They establish conceptual and procedural boundaries: what learners should know before touching the projects. - The repo’s architectural “control plane” is the curriculum sequencing itself, managed through module READMEs and cross-references. 5. **Assessment subsystem** - `assessments/` provides the evaluation layer for the curriculum. - It includes coding challenges, practical exams, quiz answer keys, and rubrics. - This creates a clear separation between **teaching content** and **evaluation artifacts**, which is important for diagrams that distinguish learning materials from grading materials. 6. **Project 101: Basic model serving** - `projects/project-101-basic-model-serving` is a compact production-style service architecture. - Main components: - `src/api.py` for the API surface - `src/model.py` for inference logic - `src/config.py` and `src/utils.py` for configuration/support code - Deployment boundaries: - `Dockerfile` and `docker-compose.yml` for local/container execution - `kubernetes/` manifests for cluster deployment - `monitoring/` for Prometheus and Grafana integration - Data flow is straightforward: client request → API → model inference → response, with metrics exported for observability. 7. **Project 102: MLOps pipeline** - `projects/project-102-mlops-pipeline` is a workflow-oriented system built around orchestration. - The main control plane is Airflow DAGs in `dags/`: - data pipeline - training pipeline - deployment pipeline - Supporting execution layers live in `src/`: - `data/` for ingestion, preprocessing, validation - `training/` for train/evaluate - `deployment/` for rollout logic - `monitoring/` for health and metrics - This project’s architecture is a **pipeline chain**: ingest/validate data → train/evaluate model → register/promote/deploy → monitor. 8. **Project 102 external ML platform dependencies** - The README indicates integration with **MLflow**, **DVC**, **PostgreSQL**, **Redis**, and **MinIO**. - Architecturally, that means the repo models a split between: - orchestration and business logic in the repo - persistent experiment/data/model state in external services - This is a classic MLOps boundary: compute is ephemeral, metadata and artifacts are persisted. 9. **Project 103: LLM deployment platform** - `projects/project-103-llm-deployment` is the most complex system in the repo. - It has three major runtime paths: - `src/llm/` for LLM serving and optimization - `src/rag/` and `src/ingestion/` for retrieval-augmented generation and document indexing - `src/api/` for the client-facing API and streaming responses - Supporting concerns: - `src/monitoring/` for metrics and cost tracking - `prompts/` for template management - `scripts/` and `notebooks/` for operational experiments and benchmarking 10. **Project 103 data flow** - The LLM project has a multi-stage data path: - documents are loaded and processed in ingestion - chunks are embedded and indexed - retrieval feeds the RAG pipeline - the API streams generated responses back to clients - In parallel, monitoring captures runtime metrics and cost signals. - This project should be diagrammed as **ingestion/indexing path + online inference path + observability path**. 11. **Infrastructure and deployment boundaries across projects** - All three projects use the same deployment patterns: - containerization via Docker - local multi-service development via Docker Compose - Kubernetes manifests for production-style deployment - The presence of `hpa.yaml`, GPU node pool manifests, services, config maps, and monitoring configs shows the repo emphasizes **operational deployment**, not just code. 12. **Observability as a first-class subsystem** - Monitoring is not centralized in one shared service; it is embedded per project. - Prometheus and Grafana appear in Projects 101 and 103, and project 102 adds model/pipeline monitoring. - This makes observability a recurring architectural concern across the repo, with metrics, dashboards, and alerts attached to each runtime system. 13. **Automation and quality gates** - GitHub Actions workflows exist both at the repo level and inside project folders. - Tests are present in each project, typically using `pytest`. - This means the repo has a layered quality model: documentation + tests + CI + deployment artifacts. 14. **How to think about the repo in a diagram** - Model the repo as three concentric layers: - **Curriculum content layer**: lessons, labs, quizzes, assessments - **Reference system layer**: three project implementations showing real architectures - **Operational tooling layer**: CI, docs, Docker/Kubernetes, monitoring, scripts - The most important boundary is between **static learning assets** and **executable project systems**. Main entry points: [`projects/project-103-llm-deployment/README.md`](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/README.md) (Project 103), [`README.md`](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/README.md) (README), [`projects/project-102-mlops-pipeline/README.md`](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-102-mlops-pipeline/README.md) (Project 102), [`lessons/mod-101-foundations/README.md`](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-101-foundations/README.md) (Mod 101) ## Diagram (Mermaid) ```mermaid flowchart TD subgraph group_grp_root["Repo guides"] node_n_readme["README
entry doc
[README.md]"] node_n_curriculum["Curriculum
roadmap doc
[CURRICULUM.md]"] node_n_start["Getting Started
onboarding doc
[GETTING_STARTED.md]"] node_n_tools["Tooling"] end subgraph group_grp_lessons["Curriculum"] node_n_mod101["Mod 101
foundations module
[README.md]"] node_n_mod104["Mod 104
kubernetes module
[README.md]"] node_n_mod106["Mod 106
mlops module
[README.md]"] node_n_mod110["Mod 110
llm infra module
[README.md]"] node_n_lessons_quiz["Lesson quizzes
module checks
[module-quiz.md]"] end subgraph group_grp_projects["Projects"] node_n_project101["Project 101
model serving app
[README.md]"] node_n_p101_src["P101 runtime
service runtime
[api.py]"] node_n_p101_deploy["P101 deploy
deployment stack
[deployment.yaml]"] node_n_project102["Project 102
mlops pipeline
[README.md]"] node_n_p102_dags["P102 DAGs
orchestration plane
[data_pipeline.py]"] node_n_p102_src["P102 ops
pipeline services
[ingestion.py]"] node_n_project103["Project 103
llm platform
[README.md]"] node_n_p103_ingest["P103 ingest
ingestion/indexing
[loader.py]"] node_n_p103_rag["P103 RAG
retrieval pipeline
[pipeline.py]"] node_n_p103_api["P103 API
streaming API
[main.py]"] node_n_p103_obs["P103 observe
metrics/cost
[metrics.py]"] end subgraph group_grp_assessments["Assessments"] node_n_assessments["Assessments
evaluation layer
[README.md]"] node_n_answer_keys["Answer keys
grading artifacts
[README.md]"] node_n_rubric["Project rubric"] end subgraph group_grp_resources["Resources"] node_n_resources["Resources
reference library
[README.md]"] end node_n_readme -->|"maps"| node_n_curriculum node_n_readme -->|"starts"| node_n_start node_n_readme -->|"includes"| node_n_assessments node_n_readme -->|"links"| node_n_resources node_n_readme -->|"packs"| node_n_tools node_n_curriculum -->|"sequences"| node_n_mod101 node_n_mod101 -->|"progresses"| node_n_mod104 node_n_mod104 -->|"progresses"| node_n_mod106 node_n_mod106 -->|"progresses"| node_n_mod110 node_n_mod101 -->|"checks"| node_n_lessons_quiz node_n_lessons_quiz -->|"validated by"| node_n_answer_keys node_n_assessments -->|"uses"| node_n_rubric node_n_project101 -->|"implements"| node_n_p101_src node_n_project101 -->|"deploys"| node_n_p101_deploy node_n_project102 -->|"orchestrates"| node_n_p102_dags node_n_project102 -->|"executes"| node_n_p102_src node_n_project103 -->|"ingests"| node_n_p103_ingest node_n_project103 -->|"retrieves"| node_n_p103_rag node_n_project103 -->|"serves"| node_n_p103_api node_n_project103 -->|"observes"| node_n_p103_obs node_n_project101 -->|"evolves to"| node_n_project102 node_n_project102 -->|"evolves to"| node_n_project103 node_n_mod101 -->|"prepares for"| node_n_project101 node_n_mod106 -->|"prepares for"| node_n_project102 node_n_mod110 -->|"prepares for"| node_n_project103 click node_n_readme "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/README.md" click node_n_curriculum "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/CURRICULUM.md" click node_n_start "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/GETTING_STARTED.md" click node_n_mod101 "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-101-foundations/README.md" click node_n_mod104 "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-104-kubernetes/README.md" click node_n_mod106 "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-106-mlops/README.md" click node_n_mod110 "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-110-llm-infrastructure/README.md" click node_n_lessons_quiz "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-108-monitoring-observability/quizzes/module-quiz.md" click node_n_assessments "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/assessments/README.md" click node_n_answer_keys "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/assessments/quizzes/answer-keys/README.md" click node_n_rubric "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/assessments/rubrics/project-assessment-rubric.md" click node_n_project101 "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-101-basic-model-serving/README.md" click node_n_p101_src "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-101-basic-model-serving/src/api.py" click node_n_p101_deploy "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-101-basic-model-serving/kubernetes/deployment.yaml" click node_n_project102 "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-102-mlops-pipeline/README.md" click node_n_p102_dags "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-102-mlops-pipeline/dags/data_pipeline.py" click node_n_p102_src "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-102-mlops-pipeline/src/data/ingestion.py" click node_n_project103 "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/README.md" click node_n_p103_ingest "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/src/ingestion/loader.py" click node_n_p103_rag "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/src/rag/pipeline.py" click node_n_p103_api "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/src/api/main.py" click node_n_p103_obs "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/src/monitoring/metrics.py" click node_n_resources "https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/resources/README.md" classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554 classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337 classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81 classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a class node_n_readme,node_n_curriculum,node_n_start,node_n_tools toneBlue class node_n_mod101,node_n_mod104,node_n_mod106,node_n_mod110,node_n_lessons_quiz toneAmber class node_n_project101,node_n_p101_src,node_n_p101_deploy,node_n_project102,node_n_p102_dags,node_n_p102_src,node_n_project103,node_n_p103_ingest,node_n_p103_rag,node_n_p103_api,node_n_p103_obs toneMint class node_n_assessments,node_n_answer_keys,node_n_rubric toneRose class node_n_resources toneIndigo ``` ## Components ### Repo guides Top-level navigation, standards, and tooling for the learning repo. - [README](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/README.md): entry doc · Primary repo overview and starting point. · `README.md` - [Curriculum](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/CURRICULUM.md): roadmap doc · Curriculum structure and module sequencing. · `CURRICULUM.md` - [Getting Started](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/GETTING_STARTED.md): onboarding doc · Setup and first-run guidance for learners. · `GETTING_STARTED.md` - **Tooling**: repo tooling · Root-level automation and packaging metadata. ### Curriculum Static instructional spine across modules 101-110. - [Mod 101](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-101-foundations/README.md): foundations module · ML infrastructure basics, cloud intro, Docker, and API development. · `lessons/mod-101-foundations/README.md` - [Mod 104](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-104-kubernetes/README.md): kubernetes module · Cluster architecture, workloads, networking, storage, and monitoring. · `lessons/mod-104-kubernetes/README.md` - [Mod 106](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-106-mlops/README.md): mlops module · Tracking, registry, deployment strategies, and governance. · `lessons/mod-106-mlops/README.md` - [Mod 110](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-110-llm-infrastructure/README.md): llm infra module · vLLM, RAG, vector databases, fine-tuning, and production LLM practices. · `lessons/mod-110-llm-infrastructure/README.md` - [Lesson quizzes](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/lessons/mod-108-monitoring-observability/quizzes/module-quiz.md): module checks · Per-module quizzes and completion checks across the curriculum. · `lessons/mod-108-monitoring-observability/quizzes/module-quiz.md` ### Projects Capstone-style reference systems showing real deployment architectures. - [Project 101](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-101-basic-model-serving/README.md): model serving app · Basic model-serving service with API, model logic, containerization, Kubernetes, and monitoring. · `projects/project-101-basic-model-serving/README.md` - [P101 runtime](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-101-basic-model-serving/src/api.py): service runtime · API and inference implementation for model serving. · `projects/project-101-basic-model-serving/src/api.py` - [P101 deploy](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-101-basic-model-serving/kubernetes/deployment.yaml): deployment stack · Docker, Compose, Kubernetes, and observability configuration. · `projects/project-101-basic-model-serving/kubernetes/deployment.yaml` - [Project 102](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-102-mlops-pipeline/README.md): mlops pipeline · Airflow-orchestrated ingestion, training, deployment, and monitoring pipeline. · `projects/project-102-mlops-pipeline/README.md` - [P102 DAGs](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-102-mlops-pipeline/dags/data_pipeline.py): orchestration plane · Airflow pipelines controlling data, training, and deployment flow. · `projects/project-102-mlops-pipeline/dags/data_pipeline.py` - [P102 ops](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-102-mlops-pipeline/src/data/ingestion.py): pipeline services · Data, training, deployment, and monitoring execution layers. · `projects/project-102-mlops-pipeline/src/data/ingestion.py` - [Project 103](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/README.md): llm platform · LLM serving platform with ingestion, RAG, API streaming, and observability. · `projects/project-103-llm-deployment/README.md` - [P103 ingest](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/src/ingestion/loader.py): ingestion/indexing · Document loading, processing, chunking, embeddings, and indexing. · `projects/project-103-llm-deployment/src/ingestion/loader.py` - [P103 RAG](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/src/rag/pipeline.py): retrieval pipeline · Retriever and RAG orchestration for online question answering. · `projects/project-103-llm-deployment/src/rag/pipeline.py` - [P103 API](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/src/api/main.py): streaming API · Client-facing API, middleware, and streaming responses. · `projects/project-103-llm-deployment/src/api/main.py` - [P103 observe](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/projects/project-103-llm-deployment/src/monitoring/metrics.py): metrics/cost · Runtime metrics, alerts, and cost tracking. · `projects/project-103-llm-deployment/src/monitoring/metrics.py` ### Assessments Quizzes, exams, rubrics, and answer keys for evaluation. - [Assessments](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/assessments/README.md): evaluation layer · Central assessment area for the curriculum. · `assessments/README.md` - [Answer keys](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/assessments/quizzes/answer-keys/README.md): grading artifacts · Quiz solutions organized by module. · `assessments/quizzes/answer-keys/README.md` - [Project rubric](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/assessments/rubrics/project-assessment-rubric.md): rubric · Shared rubric for project evaluation. · `assessments/rubrics/project-assessment-rubric.md` ### Resources Cheat sheets, FAQs, and reading lists. - [Resources](https://github.com/ai-infra-curriculum/ai-infra-engineer-learning/blob/main/resources/README.md): reference library · Cheat sheets, FAQs, and reading lists. · `resources/README.md` ## Connections - README → Curriculum: maps · Repository overview points to the curriculum structure. - README → Getting Started: starts · New learners begin with onboarding guidance. - README → Assessments: includes · Repo overview includes the evaluation layer. - README → Resources: links · Reference materials are part of the repo entry point. - README → Tooling: packs · Root tooling supports the whole repository. - Curriculum → Mod 101: sequences · Curriculum begins with the foundations module. - Mod 101 → Mod 104: progresses · Learners move from foundations into Kubernetes. - Mod 104 → Mod 106: progresses · Kubernetes prepares learners for MLOps concepts. - Mod 106 → Mod 110: progresses · MLOps leads into LLM infrastructure. - Mod 101 → Lesson quizzes: checks · Modules are paired with quiz checkpoints. - Lesson quizzes → Answer keys: validated by · Quiz answers provide the grading reference. - Assessments → Project rubric: uses · Projects are assessed against a shared rubric. - Project 101 → P101 runtime: implements · The runtime is centered on API and model code. - Project 101 → P101 deploy: deploys · Container and cluster deployment are first-class. - Project 102 → P102 DAGs: orchestrates · Airflow DAGs control the MLOps workflow. - Project 102 → P102 ops: executes · Pipeline stages are implemented in source services. - Project 103 → P103 ingest: ingests · Documents enter through the ingestion path. - Project 103 → P103 RAG: retrieves · RAG coordinates retrieval and generation. - Project 103 → P103 API: serves · The API exposes streaming inference to clients. - Project 103 → P103 observe: observes · Monitoring and cost tracking run alongside inference. - Project 101 → Project 102: evolves to · The projects form a progression from serving to workflow automation. - Project 102 → Project 103: evolves to · The final project extends into LLM platform architecture. - Mod 101 → Project 101: prepares for · Foundations support the basic model-serving project. - Mod 106 → Project 102: prepares for · MLOps lessons align with the pipeline project. - Mod 110 → Project 103: prepares for · LLM infrastructure lessons align with the deployment platform.