Large language models

Build and operate production AI platforms on Kubernetes. Learn to manage NVIDIA GPUs, serve and optimize LLMs with vLLM, run training and batch workloads, and design secure, observable, multi-tenant infrastructure for AI at scale.

Discover the mathematics powering the future of Artificial Intelligence and cybersecurity.Algebraic Foundations of AI: Groups, Rings, and Fields in Security & Cryptography reveals how abstract algebra forms the backbone of modern AI security, cryptographic systems, blockchain technologies, privacy-preserving machine learning, federated learning, and post-quantum cryptography.

⚡ THE BRUTAL TRUTH: 90% of job seekers use AI the same way — basic prompts, generic CVs, zero strategy. The other 10%? They're getting callbacks while you wait. This guide puts you in the 10%.

A hands-on guide to designing, building, testing, and deploying secure stdio and SSE MCP servers in Python and TypeScript (447 manuscript pages).

Threat → Control → Requirement → Verification. The field manual to read before you ship an autonomous agent.

AI-collaboration interviews are not about prompting better. They are about controlling the process. Learn a four-step framework to ask better questions, write a clear spec, guide AI-assisted building, and prove correctness with independent review and tests.

Unlock the full potential of Claude Fable 5 with a practical guide built for developers who are shipping real AI applications. From prompt engineering and agent orchestration to RAG, memory, safety, evaluation, and production deployment, this definitive reference delivers proven patterns, hands-on examples, and reusable templates to help you build reliable, scalable AI systems with confidence.

Linear Programming and AI Optimization Models (VOL-3) delivers practical mastery in scheduling, supply chain, logistics, hybrid AI-OR systems, large-scale cloud optimization, and emerging technologies. With Python implementations, industry case studies, and forward-looking research trends, this volume turns theory into powerful real-world solutions.

Take your optimization skills to the next level with AI.Linear Programming and AI Optimization Models (VOL-2) dives deep into the algorithms driving modern Machine Learning and Intelligent Systems. Master Gradient Descent, Adam, Reinforcement Learning, Genetic Algorithms, Particle Swarm Optimization, Constraint Satisfaction, and advanced Network Models.

Unlock the power of optimization in the age of AI."Linear Programming and AI Optimization Models" delivers a masterful blend of classical Operations Research and modern Machine Learning applications. From the elegant Simplex Method to advanced decomposition techniques and KKT conditions, this Volume-1 builds a rock-solid foundation while demonstrating how these powerful algorithms .

Learn how to build reliable AI agents with PydanticAI, from simple chatbots to production-ready multi-agent systems. With practical examples, clear explanations, and hands-on projects, this book helps you write AI applications that are structured, testable, and easy to maintain.

Enterprise Retrieval-Augmented Generation with C# is a practical guide to building production-ready AI applications in the modern .NET ecosystem. Learn how to design scalable, secure, and high-performance RAG systems through real-world C# examples, proven architectures and enterprise best practices.

Large language models are not the architecture. Enterprise Intelligence Architecture shows how memory, governance, orchestration, validation, and model routing work together to build AI systems that are reliable, explainable, and production-ready. A practical blueprint for architects, AI engineers, and technical leaders.

This practical guide shows you how to build production-ready speech recognition applications from the ground up. Learn how to use Voice Activity Detection, choose the right ASR models, build real-time inference pipelines, and deploy scalable systems with hands-on examples and modern open-source tools.

Build real-world AI applications with Python—not just demos. Learn LLM APIs, RAG, embeddings, vector search, AI agents, prompt engineering, security, deployment, and production-ready architectures through practical projects and expert guidance. Gain the skills to build, ship, and scale modern AI systems with confidence.