AI Platform Engineering

There's a growing gap in the AI world that nobody talks about enough.

On one side, there are brilliant models — foundation models, fine-tuned specialists, autonomous agents doing remarkable things. On the other side, there's raw infrastructure — GPUs, servers, networks, storage. What's missing is the platform layer in between: the systems that take those models and that hardware and turn them into something an organization can actually rely on.

That layer is what this book is about.

AI Platform Engineering is a hands-on, code-driven guide to designing, building, and operating the systems that power production AI. It's written by an engineer who builds AI platforms daily — not from the perspective of a consultant who parachutes in, but from someone who lives with the consequences of every architectural decision.

What you'll learn:

• Model Serving Architecture — How inference engines like vLLM, TensorRT-LLM, and SGLang actually work. Continuous batching, KV cache management, PagedAttention, and the serving stack from model weights to API response.
• Inference Optimization — Quantization (FP16 to INT4), speculative decoding, semantic caching, and model routing. Getting more intelligence per GPU dollar.
• GPU Orchestration — Kubernetes for AI workloads, Dynamic Resource Allocation, job queuing with Kueue, multi-tenant GPU scheduling, and when to use Slurm or Ray instead.
• The Data Layer — Vector databases, RAG pipeline architecture, embedding stores, and storage systems that can keep GPUs fed without becoming bottlenecks.
• AI Observability — Why the three pillars aren't enough. Monitoring non-deterministic systems, output quality tracking, distributed tracing for AI pipelines, and alerting that actually helps.
• AI FinOps — Token economics, cost-per-inference tracking, model routing for cost optimization, and the TCO math for on-premises vs. cloud.
• Security & Governance — Prompt injection defenses, multi-tenant isolation, data sovereignty, model governance, and guardrails as platform services.
• Agent Infrastructure — What agentic AI means for platform engineers: session management, tool execution sandboxes, cost control, and observability for autonomous workflows.
• Developer Experience — Building a platform that engineers actually want to use. Self-service interfaces, golden paths, templates, and feedback loops.
  

What makes this book different:

This is not a survey of tools. It's not a strategy book for executives. It's an engineering book that treats AI platform building as a craft — with the same rigor that Designing Data-Intensive Applications brought to distributed systems.

Every chapter includes working code, architecture diagrams, and a "Builder's Notebook" section with hands-on exercises. The companion GitHub repository provides complete, runnable implementations for every major concept.

Who this book is for:

• Platform engineers whose teams just got tasked with "build us an AI platform"
• ML engineers who are tired of the gap between notebook prototypes and production systems
• Engineering managers building AI platform teams who need the technical depth to make architectural decisions
• DevOps/SRE engineers entering the AI infrastructure space
• Anyone who believes that the systems behind AI matter as much as the models themselves
  

Who this book is NOT for:

• Data scientists looking for a model training guide
• Executives looking for AI strategy advice
• Beginners looking for a "What is Machine Learning?" introduction
  

This book assumes you're a working engineer who is comfortable with distributed systems concepts, has used containers and orchestration tools, and wants to go deep on the specific challenges of building platforms for AI workloads.