AI Platform Engineering Handbook

A practical handbook for building enterprise AI platforms.

Written in simple English for platform engineers, SREs, MLOps engineers, AI engineers, and architects. Every chapter starts from a real problem, explains the idea step by step, and ends with a hands-on lab and interview questions.

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Why this handbook

Most AI content is either too academic or too shallow. This handbook sits in the middle. It explains how things actually work, then shows how to run them in production.

You do not need an ML background. If you can read a systems design doc, you can read this.

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Contents

Volume 1 — AI Evaluations

1. Introduction to AI Evaluations
2. Rubrics: How to Measure AI Quality
3. Evaluation Methods
4. Building an LLM-as-a-Judge
5. Evaluating RAG Systems
6. AI Evaluation Metrics
7. Building an End-to-End AI Evaluation Pipeline
8. AI Observability and Monitoring
9. Continuous Improvement and AI Experimentation
10. Building an Enterprise AI Evaluation Platform
11. AI Safety, Security, and Governance
12. Enterprise AI Case Study: Building an HR Assistant

Volume 2 — Enterprise AI Systems

1. How Large Language Models (LLMs) Really Work
2. Enterprise RAG Deep Dive
3. Context Engineering: The New Prompt Engineering
4. AI Memory Systems
5. AI Agents and Agentic AI
6. Model Context Protocol (MCP) and AI Gateways
7. Enterprise AI Evaluation (Evals)
8. AI Observability and Monitoring
9. Enterprise AI Security and Guardrails
10. Enterprise AI Platform Architecture
11. Operating Enterprise AI Platforms (AI SRE & MLOps)

Volume 3 — Advanced AI Engineering

1. Fine-Tuning Large Language Models
2. Building High-Quality Datasets for AI
3. Synthetic Data Generation for Enterprise AI
4. Reinforcement Learning and Alignment for LLMs
5. Reasoning Models and Test-Time Scaling
6. Advanced Retrieval-Augmented Generation (Advanced RAG)
7. Multimodal AI and Vision Language Models (VLMs)
8. AI Agents at Scale: Multi-Agent Systems, Agentic Workflows, and Swarm Intelligence
9. AI Coding Assistants and Autonomous Software Engineering
10. AI Security, Governance, Risk, and Responsible AI
11. AI Observability, Monitoring, and Continuous Evaluation
12. Enterprise AI Platform Architecture (End-to-End Blueprint)

Volume 4 — AI Infrastructure and Platform Engineering

1. GPU Architecture for Large Language Models
2. Transformer Execution: From Prompt to Token
3. LLM Inference Optimization
4. Quantization, Model Compression, and Efficient LLM Deployment
5. High-Performance LLM Serving
6. Kubernetes for AI Infrastructure
7. Distributed AI Systems: Multi-GPU Training and Inference
8. AI Infrastructure Observability and Performance Engineering
9. AI Platform Security, Governance, and Responsible AI
10. Enterprise AI Platform Reference Architecture (End-to-End)

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Repository layout

chapters/       The four volumes, one folder per volume
architecture/   Reference architectures (Azure, Kubernetes)
diagrams/       Diagram sources and exports
labs/           Hands-on labs (each chapter also has its own lab section)
interview/      Interview questions collected from every chapter
cheat-sheets/   One-page summaries
glossary/       Terms explained in plain words
resources/      Papers, books, blogs, and projects worth reading

Who this is for

• AI engineers
• Platform engineers and SREs
• MLOps engineers
• Cloud architects
• Software engineers moving into AI
• Students

How to read it

Read the volumes in order if you are new. If you already run AI systems, jump straight to the chapter you need. Each chapter stands on its own.

Diagrams

Key architecture diagrams live in diagrams/. In-chapter flowcharts use Mermaid, which GitHub renders automatically.

Contributing

Fixes and improvements are welcome. See CONTRIBUTING.md.

License

MIT