Overview

Most AI projects never make it to production. The ones that do often fail within 18 months. AI Systems Engineering 2026 is the complete, hands-on guide to building AI that actually works - in the real world, at scale, under pressure. Whether you are writing your first machine learning pipeline or designing infrastructure for millions of daily predictions, this book meets you where you are and takes you to where you need to be. WHAT YOU WILL LEARN: ► Why most AI projects fail - and the three engineering laws that prevent it ► How to design production-grade AI architecture from day one ► Building robust data pipelines with the Medallion architecture and real-time streaming ► MLOps in practice: experiment tracking, reproducible training, continuous deployment ► Containerising and deploying models with Docker, Kubernetes, and canary releases ► Cloud infrastructure, GPU right-sizing, and cost optimisation strategies ► LLM engineering: RAG systems, prompt versioning, token cost management, and routing ► Performance tuning: TensorRT, quantisation (GPTQ/INT8), knowledge distillation ► Distributed inference, federated learning, and edge AI deployment ► Monitoring, drift detection, and automated retraining systems ► AI security, governance, EU AI Act compliance, and model card documentation ► Agentic AI: building reliable autonomous pipelines with LangGraph and multi-agent patterns FULLY UPDATED FOR 2026: This edition covers the tools and techniques defining production AI right now - including frontier LLM engineering, agentic systems, the EU AI Act enforcement requirements, and the latest optimisation methods across the full inference stack. Every chapter includes working code examples in Python, real-world case studies from organisations including Netflix, Spotify, Klarna, Capital One, Google, and Microsoft, and practical decision frameworks you can apply immediately. This is not a research book. It is an engineering reference - built for people who need to ship.

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