WASI

Build the engineering foundations required to design and develop production-grade AI systems—from Python and FastAPI to databases, security, semantic search, RAG, LLM architecture, and AI application performance.Go beyond Python programming and start thinking like an AI Engineer.The AI Engineer — Volume I takes you from professional Python engineering to the foundations of modern AI application development.You will build the engineering knowledge required to create reliable AI systems—not just experimental notebooks or simple API calls.A project-based AI engineering book covering Python production engineering, FastAPI, PostgreSQL, authentication, testing, reliability, embeddings, semantic search, RAG, LLM architecture, AI pipelines, caching, and performance optimization.AI EngineeringAI EngineerArtificial IntelligenceGenerative AILLMLarge Language ModelsPythonFastAPIRAGRetrieval Augmented GenerationSemantic SearchEmbeddingsVector SearchLLM ApplicationsAI SystemsMachine Learning EngineeringBackend EngineeringProduction AIAI ArchitectureSoftware Engineering

About the Book Running an LLM is one problem. Understanding its memory footprint, execution phases, and serving behavior is another.LLM Inference Engine Architecture explores the engineering behind language-model inference—from prefill and decode to KV-cache management, attention kernels, quantization, and request scheduling.Written for software engineers and ML engineers with a working knowledge of Python and transformer fundamentals, the book connects mathematical explanations with implementation examples and serving-system design considerations.What You Will ExploreInference fundamentals: Trace the request lifecycle and understand the different computational characteristics of prefill and decode.Memory budgeting: Work through model-weight and KV-cache estimates, including the effects of context length, KV-head count, precision, and concurrent sequences.Attention and memory management: Explore PagedAttention, non-contiguous KV allocation, and the relationship between attention computation and memory access.Quantization: Examine weight and KV-cache precision choices, including FP8 and low-bit weight-quantization approaches.Serving architecture: Study continuous batching, request scheduling, multi-GPU execution, and latency and throughput metrics.Kernel development: Explore CUDA and Triton concepts, kernel fusion, and a quantized matrix-multiplication case study.The book includes numerical examples, reference code, and an educational asynchronous serving simulator. These examples are intended to explain mechanisms and trade-offs—not to replace hardware-specific profiling or production validation.Read the sample to review the mathematical level, coding style, and scope before purchasing.

Behind every breakthrough in Generative AI isn't magic—it's mathematics."​Ever wondered what actually happens inside a Large Language Model when it generates the next token? Beyond the hype and high-level API calls lies an elegant foundation of probability, vector geometry, and linear algebra.​In The Mathematics of Generative AI, Ahmed Adawy strips away the academic jargon to reveal the exact core equations driving modern LLMs. From conditional probability and high-dimensional embeddings to Query-Key-Value attention mechanisms, this capsule equips software engineers and AI practitioners with the intuition needed to build, debug, and innovate with confidence.​Stop treating AI like a black box. Master the equations beneath the intelligence.

Build a fully functional semantic search engine from first principles using pure Python and NumPy—no heavy AI frameworks, no vector databases, just pure intuition and mathematics.

Tired of paying the "container tax" for bursty, event-driven workloads? It's time to elevate your cloud architecture. Building Serverless Applications with WebAssembly & Spin walks you through constructing a robust, multi-component Rust app from scratch. You will master Spin, learn about the underlying Wasm Component Model, and ship your app seamlessly to both Kubernetes and edge runtimes. Ditch heavy base images, eliminate cold starts, and master the future of lightweight, secure serverless compute.

Build fast, secure and portable applications with WASI. Start with the fundamentals, then move into components, WIT, Wasmtime and real production patterns using Rust, C, Go and JavaScript. From sandboxed plugins to serverless and edge workloads, this practical guide gives you the skills to take WebAssembly from your first experiment to production.