Ai Engineering
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Ai Engineering
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Author : Chip Huyen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-12-04
Ai Engineering written by Chip Huyen and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-04 with Computers categories.
Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI. AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).
Engineering Ai Systems
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Author : Len Bass
language : en
Publisher: Addison-Wesley Professional
Release Date : 2025-03-03
Engineering Ai Systems written by Len Bass and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-03 with Computers categories.
Master the Engineering of AI Systems: The Essential Guide for Architects and Developers In today's rapidly evolving world, integrating artificial intelligence (AI) into your systems is no longer optional. Engineering AI Systems: Architecture and DevOps Essentials is a comprehensive guide to mastering the complexities of AI systems engineering. This book combines robust software architecture with cutting-edge DevOps practices to deliver high-quality, reliable, and scalable AI solutions. Experts Len Bass, Qinghua Lu, Ingo Weber, and Liming Zhu demystify the complexities of engineering AI systems, providing practical strategies and tools for seamlessly incorporating AI in your systems. You will gain a comprehensive understanding of the fundamentals of AI and software engineering and how to combine them to create powerful AI systems. Through real-world case studies, the authors illustrate practical applications and successful implementations of AI in small- to medium-sized enterprises across various industries, and offer actionable strategies for designing, building, and operating AI systems that deliver real business value. Lifecycle management of AI models, from data preparation to deployment Best practices in system architecture and DevOps for AI systems System reliability, performance, and security in AI implementations Privacy and fairness in AI systems to build trust and achieve compliance Effective monitoring and observability for AI systems to maintain operational excellence Future trends in AI engineering to stay ahead of the curve Equip yourself with the tools and understanding to lead your organization's AI initiatives. Whether you are a technical lead, software engineer, or business strategist, this book provides the essential insights you need to successfully engineer AI systems. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Ai Engineering For Beginners
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Author : Peter E Poisson
language : en
Publisher: Independently Published
Release Date : 2025-07-11
Ai Engineering For Beginners written by Peter E Poisson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-11 with Computers categories.
AI isn't just for PhDs and Silicon Valley giants anymore. AI Engineering for Beginners: From ML Foundations to Production Systems is your practical, no-fluff roadmap to mastering artificial intelligence, from the ground up. Whether you're a coding enthusiast, aspiring AI engineer, or tech professional pivoting into machine learning, this book takes you by the hand guiding you through core ML principles, hands-on projects, and real-world deployment strategies that companies use today. Inside, you won't just learn theory you'll build projects, optimize models, and gain production-ready skills that are in high demand across industries. From foundational machine learning concepts to MLOps, cloud scaling, and advanced AI agents like LLMs, you'll discover exactly how to design, develop, and deliver AI solutions in a structured, beginner-friendly way. By the end, you won't just understand AI you'll be able to engineer it. Inside This Book, You'll Learn How To: Master core machine learning concepts and build your first working models using Python & scikit-learn. Navigate essential AI tools like TensorFlow, PyTorch, and MLflow without the confusion. Design scalable AI pipelines and automate workflows with cutting-edge MLOps techniques. Deploy real AI systems using FastAPI, Docker, and cloud services like AWS and GCP. Explore Large Language Models (LLMs), prompt engineering, and AI agent frameworks for modern AI applications. Start your AI journey today grab your copy and begin building intelligent systems that make a real impact!
Ai Engineering
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Author : Chip Huyen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-12-04
Ai Engineering written by Chip Huyen and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-04 with Computers categories.
Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI. AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).
Ai Engineering Essentials
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Author : Thomas Robinson
language : en
Publisher: Independently Published
Release Date : 2025-06-17
Ai Engineering Essentials written by Thomas Robinson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.
What does it really take to build AI that works - not just in the lab, but in the messy, unpredictable world of real business and real people? Have you ever wondered why so many promising AI projects stall before they see the light of day? Or why that brilliant proof of concept struggles to handle live customers, edge cases, or compliance rules once it leaves the prototype stage? AI ENGINEERING ESSENTIALS: The Applied AI Engineer is your honest guide to closing that painful gap. Ask yourself: Do you know how to choose the right problem for AI - or are you throwing models at the wrong questions? Are your data pipelines truly trustworthy and reproducible, or do they crumble under production loads? Can your models survive the real world, or do they drift, decay, and silently fail while no one notices until it's too late? Do you have the tools, teams, and governance in place to handle ethical dilemmas, explainability demands, and unexpected outages? More importantly, do you know how to lead AI projects so they deliver value again and again, not just once? Thomas Robinson doesn't just show you the theory - he walks you through practical, repeatable engineering patterns to take AI from an idea to a resilient, scalable system. Whether you're a software engineer adding ML to your toolkit, a technical lead managing your first AI product, or a product owner trying to connect ambitious AI goals with practical delivery, this book speaks your language. Inside, you won't find abstract hype. You'll find real-world strategies for: Designing cross-functional teams that actually collaborate, not just coexist. Building robust data pipelines and reproducible experiments. Deploying, serving, and monitoring models without babysitting them 24/7. Managing technical debt so you don't have to rewrite your entire stack every six months. Keeping your AI explainable, compliant, and ethical - because reputation matters more than ever. Scaling AI systems from scrappy MVP to global rollout, without sacrificing reliability or blowing up your cloud bill. Think of this book not as a dry manual, but as a trusted senior engineer in your corner - asking tough questions, sharing battle-tested lessons, and pushing you to build AI the right way from day one. Are you ready to stop experimenting endlessly and start engineering AI that earns trust, delivers ROI, and survives in production? Then this is the book you've been searching for. Grab your copy of AI ENGINEERING ESSENTIALS: The Applied AI Engineer today - and become the AI engineer your organization can truly rely on.
Practical Ai Engineering
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Author : Juno Darian
language : en
Publisher: Independently Published
Release Date : 2025-07-25
Practical Ai Engineering written by Juno Darian and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-25 with Computers categories.
Build, Deploy, and Scale Real-World AI Systems-From Foundation Models to Full-Stack Production Pipelines Are you ready to move beyond tutorials and toy models into the real world of scalable, production-ready AI? Practical AI Engineering is your complete, no-fluff, hands-on guide to building modern AI applications from scratch to mastery. Whether you're aiming to become a full-stack AI engineer, deploy cutting-edge LLMs (Large Language Models), or bring real-time Retrieval-Augmented Generation (RAG) systems into production, this book takes you there-step by step. Written for engineers, ML practitioners, and developers who want more than just theoretical knowledge, this book equips you with battle-tested workflows, system design patterns, and toolchains used by top AI teams. What You'll Master Inside This Book: AI Engineering from the Ground Up - Learn what AI engineering really means: beyond models, into systems - Master the end-to-end AI lifecycle (Design → Deploy → Maintain) - Think like a systems engineer for real-world impact The Full Toolkit for Modern AI Engineers - Python patterns, TensorFlow vs. PyTorch, FastAPI, HuggingFace, LangChain - Data pipelines, Docker, Kubernetes, and GitOps workflows - Experiment tracking, versioning, and CI/CD automation LLMs, Transformers, and Prompt Engineering in Practice - Understand how GPT models work and scale - Use OpenAI APIs and HuggingFace models efficiently - Apply few-shot, chain-of-thought, and retrieval-augmented strategies - Implement LLMOps for inference, caching, and cost control Retrieval-Augmented Generation (RAG) and GraphRAG - Chunking, embeddings, and vector databases (FAISS, Pinecone, Qdrant) - Build RAG systems with LangChain, FastAPI, and custom memory - Go beyond text: create knowledge-augmented LLMs with Neo4j and GraphRAG - Complete projects: Legal QA bots, research assistants, scalable chatbots Agentic AI and Multi-Tool Orchestration - Build agents that use tools like Web Browsing, SQL, and PDFs - Explore LangChain Agents, OpenAgents, AutoGen frameworks - Monitor hallucinations, plan actions, and design recovery flows - Ensure safety, logging, and performance in agentic systems Production-Ready Deployment with Docker & Kubernetes - Package LLMs and APIs into portable containers - Use docker-compose and Helm charts for orchestration - Deploy scalable clusters with GPU access and autoscaling - Implement health probes, registries, and versioned microservices Observability, Evaluation & Continuous Delivery - Monitor LLM drift, RAG relevance, and real-time model metrics - Run A/B tests, feedback loops, and prompt re-ranking - Automate your ML pipelines using GitHub Actions + MLflow - Set up failover, alerts, and canary deployments Ethical and Global AI Deployment - Handle bias, safety, privacy, and data sovereignty - Harden APIs against adversarial prompts and jailbreaking - Deploy inclusive systems across global and non-Western contexts Among others.. BONUS: Companion Project Repositories + Cheat Sheets Real projects: RAG chatbots, GraphRAG assistants, LLM agents If you're looking for a deeply practical, industry-relevant, and project-driven book to help you master modern AI engineering-this is it. Perfect for: - AI/ML engineers and full-stack developers - Backend engineers diving into LLMs and RAG - Technical founders building AI-powered products Join the future of AI development - become a practical AI Engineer.
The Ai Engineer S Guide To Surviving The Eu Ai Act
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Author : Larysa Visengeriyeva
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-06-27
The Ai Engineer S Guide To Surviving The Eu Ai Act written by Larysa Visengeriyeva and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-27 with Business & Economics categories.
With the introduction of the EU AI Act, companies employing AI systems face a new set of comprehensive and stringent regulations. Dr. Larysa Visengeriyeva offers a much-needed guide for navigating these unfamiliar regulatory waters to help you meet compliance challenges with confidence. From explaining the legislative framework to sharing strategies for implementing robust MLOps and data governance practices, this wide-ranging book shows you the way to thrive, not just survive, under the EU AI Act. It's an indispensable tool for engineers, data scientists, and policymakers engaged in or planning for AI deployments within the EU. By reading, you'll gain: An in-depth understanding of the EU AI Act, including the four risk categories and what they mean for you Strategies for compliance, including practical approaches to achieving technical readiness Actionable advice on applying MLOps methodologies to ensure ongoing compliance Insights on the implications of the EU's pioneering approach to AI regulation and its global effects
Ai Engineering For Beginners
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Author : James Douglas
language : en
Publisher:
Release Date : 2025-04-17
Ai Engineering For Beginners written by James Douglas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-17 with Computers categories.
Ready to dive into the world of artificial intelligence-but not sure where to start? This beginner-friendly guide gives you the tools, confidence, and clear step-by-step strategies to build real-world AI systems from scratch-no PhD required. Whether you're a curious learner, career changer, or aspiring developer, AI Engineering for Beginners walks you through the essentials of machine learning, neural networks, data pipelines, model deployment, and responsible AI practices. You'll build your first models, learn the tools professionals use, and gain the skills to pursue a career in one of the most exciting fields in tech. Inside this book, you'll learn: What AI engineering is-and how it's different from research or data science How machine learning works (including supervised, unsupervised, and reinforcement learning) How to collect, clean, and prepare real-world data How to build, train, and evaluate your own AI models How to deploy models using tools like Docker, FastAPI, and cloud platforms How to think critically about AI ethics, bias, and transparency What jobs exist in AI-and how to build a portfolio that gets you hired Written in a friendly, accessible style inspired by bestselling educators, this book turns intimidating concepts into understandable, actionable knowledge. Whether you're just curious or ready to start building, AI Engineering for Beginners is your launchpad into the future of intelligent systems.
The Ai Engineering Bible
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Author : Clifford Harrison
language : en
Publisher: Independently Published
Release Date : 2025-10-27
The Ai Engineering Bible written by Clifford Harrison and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-27 with Computers categories.
The AI Engineering Bible is your definitive guide to mastering the fast-evolving world of Artificial Intelligence from an engineering perspective. Whether you're a beginner eager to enter the AI field or a seasoned developer looking to future-proof your skills, this all-in-one resource offers in-depth knowledge, practical frameworks, and real-world applications. Covering everything from machine learning algorithms, data pipelines, model deployment, and AI system architecture to ethical considerations and cutting-edge tools, this guide bridges theory with hands-on practice. Designed to be both educational and actionable, it serves as your go-to reference for building, scaling, and maintaining intelligent systems across industries. ✅Inside you'll find: -✔️ Foundations of AI & machine learning ✔️- Tools, frameworks & best practices -✔️ Model training, evaluation, and optimization -✔️ Scalable deployment strategies (MLOps) - ✔️Real-world AI engineering workflows -✔️ Ethics, governance, and responsible AI - ✔️Case studies, code examples & checklists Whether you're building smart apps, training models, or designing AI systems, The AI Engineering Bible equips you with the skills, mindset, and resources needed to thrive in the age of intelligence.
Ai Engineering Mastery
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Author : Kenneth W Moe
language : en
Publisher: Independently Published
Release Date : 2025-07-11
Ai Engineering Mastery written by Kenneth W Moe and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-11 with Computers categories.
Master the Art of AI Engineering From Bold Ideas to Intelligent Systems That Deliver Results. In today's rapidly evolving tech landscape, artificial intelligence isn't just a buzzword, it's a necessity. Yet, many engineers struggle with moving beyond theory to actually building, scaling, and maintaining intelligent systems that perform reliably in production. This book bridges that gap. AI Engineering Mastery takes you on a practical, actionable journey through the complete AI system lifecycle from identifying real-world problems and training models, to deploying solutions at scale and ensuring they keep learning over time. Whether you're an engineer, developer, or tech leader, you'll discover how to transform complex AI projects into resilient, business-ready systems. Through hands-on guidance, expert insights, and field-tested frameworks, this book empowers you to engineer AI systems that are not only cutting-edge, but also ethical, scalable, and impactful in the long term. Inside this book, you'll learn how to: Design AI solutions that solve real-world problems while minimizing risk. Deploy and monitor AI models at scale using proven MLOps practices. Master the art of integrating Large Language Models (LLMs) and Generative AI into applications. Engineer responsible, ethical AI systems that prioritize fairness, transparency, and sustainability. Future-proof your career with strategies for adapting to AI trends and evolving technologies. Get your copy today and start building AI systems that are built to last!