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Hyndman R: Forecasting

Forecasting

Principles and Practice, the Pythonic Way

As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge—connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems.
The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape—from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built.
By the final chapter, readers will not only understand agentic systems—they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, Agentic AI for Engineers equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents. 
What You Will Learn

<li>A practical introduction to Machine Learning and Generative AI, tailored for engineers

</li><li>Conceptualize, design, and build autonomous AI agents from scratch—even with a minimal AI background.

</li><li>The core principles of Agentic AI, including goals, environments, actions, and feedback loops

</li><li>Understand different Agentic AI frameworks and their applications.

</li><li>Integrate agentic systems into real-world applications using hands-on coding examples

</li><li>Review strategies for ensuring safe, ethical, and auditable agent behavior in production environments




Who This Book Is For
Primary audience includes Software Engineers, DevOps, and Data Engineers curious about building intelligent, autonomous systems but who lack formal AI training; Technical Product Managers and Engineering Leaders looking to understand and implement Agentic AI in real-world systems; System Architects and Automation Engineers exploring the shift from traditional automation to intelligent agent-based architectures. 
AI/ML Enthusiasts and self-learners, Engineering and computer science students, and professionals in emerging tech domains who want to build or deploy autonomous agents will also benefit from this book.
 


</li>

As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge—connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems.The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape—from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built.
 By the final chapter, readers will not only understand agentic systems—they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, 
 Agentic AI for Engineers
 equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents. &nbsp;
 What You Will Learn<ul><li>A practical introduction to Machine Learning and Generative AI, tailored for engineers</li><li>Conceptualize, design, and build autonomous AI agents from scratch—even with a minimal AI background.</li><li>The core principles of Agentic AI, including goals, environments, actions, and feedback loops</li><li>Understand different Agentic AI frameworks and their applications.</li><li>Integrate agentic systems into real-world applications using hands-on coding examples</li><li>Review strategies for ensuring safe, ethical, and auditable agent behavior in production environments</li></ul>Who This Book Is ForPrimary audience includes Software Engineers, DevOps, and Data Engineers curious about building intelligent, autonomous systems but who lack formal AI training; Technical Product Managers and Engineering Leaders looking to understand and implement Agentic AI in real-world systems; System Architects and Automation Engineers exploring the shift from traditional automation to intelligent agent-based architectures. AI/ML Enthusiasts and self-learners, Engineering and computer science students, and professionals in emerging tech domains who want to build or deploy autonomous agents will also benefit from this book.&nbsp;&nbsp;

Architecting Goal-Driven Systems

Agentic AI for Engineers

In today’s AI-driven business environment, the ability to translate data into sound decisions is increasingly important. This textbook introduces the foundations and practical applications of business analytics, machine learning, and artificial intelligence. Readers learn how to explore and visualize business data, build and evaluate predictive models, and apply analytical methods to support business decisions.The book progresses from core concepts such as data visualization, optimization, model evaluation, supervised learning, and unsupervised learning to advanced topics in deep learning, computer vision, generative AI, text mining, natural language processing, and large language models. It also addresses the limitations and ethical challenges of AI. Step-by-step explanations, business examples, and Python exercises help readers connect analytical concepts with real-world applications.Designed for students and professionals with little or no prior experience in programming or machine learning, the book develops skills progressively from basic concepts to advanced methods. It prepares readers to select, implement, interpret, and evaluate AI-based analytical approaches for a wide range of business problems.Hyunhwan “Aiden” Lee
 is Assistant Professor of Marketing at California State University, Long Beach, USA. His research focuses on marketing analytics, brand and content analytics, and the application of artificial intelligence to business and marketing problems. His work uses machine learning, natural language processing, computer vision, location-based data analysis, large-scale data analysis, and mathematical modeling.
 Reo Song
 &nbsp;is Professor of Marketing and Director of the M.S. in Marketing Analytics program at California State University, Long Beach. His research focuses on machine learning, AI, business analytics, and digital marketing, with publications in leading journals including&nbsp;
 Journal of Marketing
 &nbsp;and&nbsp;
 Production and Operations Management
 . He has advised organizations on AI and machine learning applications, delivers frequent invited talks on AI and business analytics, and contributes regularly to newspaper columns on AI and digital transformation.

In today’s AI-driven business environment, the ability to translate data into sound decisions is increasingly important. This textbook introduces the foundations and practical applications of business analytics, machine learning, and artificial intelligence. Readers learn how to explore and visualize business data, build and evaluate predictive models, and apply analytical methods to support business decisions.The book progresses from core concepts such as data visualization, optimization, model evaluation, supervised learning, and unsupervised learning to advanced topics in deep learning, computer vision, generative AI, text mining, natural language processing, and large language models. It also addresses the limitations and ethical challenges of AI. Step-by-step explanations, business examples, and Python exercises help readers connect analytical concepts with real-world applications.Designed for students and professionals with little or no prior experience in programming or machine learning, the book develops skills progressively from basic concepts to advanced methods. It prepares readers to select, implement, interpret, and evaluate AI-based analytical approaches for a wide range of business problems.

An Advanced Guide to Data-Driven Decision Making

Business Analytics and Artificial Intelligence

De Gruyter Textbook

This book is a textbook about calculus of one variable. It contains proofs of all the theorems, propositions etc but also Python codes in order to solve many calculus problems. To produce these codes we use AI and show to the reader how to do that.&nbsp;
This book aims to supplement the existing bibliography concerning single-variable calculus. Material including Python code for both symbolic and approximate computations. Our goal is to appropriately use artificial intelligence to generate this code and demonstrate to students how this is approximately done. This book is extremely useful for first-year science students, their professors, and others.

This book is a textbook about calculus of one variable. It contains proofs of all the theorems, propositions etc but also Python codes in order to solve many calculus problems. To produce these codes we use AI and show to the reader how to do that.&nbsp;This book aims to supplement the existing bibliography concerning single-variable calculus. Material including Python code for both symbolic and approximate computations. Our goal is to appropriately use artificial intelligence to generate this code and demonstrate to students how this is approximately done. This book is extremely useful for first-year science students, their professors, and others.

with Python and AI

Calculus of One Variable

DK Complete Courses

Learning to code has never been easier than with this innovative visual guide to computer programming for beginners. In our digital world, coding skills are in high demand and the need for programmers is still growing. Taking the first steps in learning to code may seem daunting but by using a graphic method to break code into small chunks, this book brings essential skills easily within reach. Each chapter explores one of the most popular languages, with practical programming projects to show you how programming works. You'll learn to think like a programmer by breaking a problem down into parts, before turning those parts into lines of code. Short, easy-to-follow steps then show you, piece by piece, how to build a complete program. There are challenges for you to tackle to build your confidence before moving on. Written by a team of expert coders and coding teachers, the Beginner's Step-by-Step Coding Course is the ideal way to get to grips with coding.

Learn Computer Programming the Easy Way

Beginner's Step-by-Step Coding Course

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Build AI Models from Scratch (No PhD Required) Deep Learning Crash Course is a fast-paced, thorough introduction that will have you building today’s most powerful AI models from scratch. No experience with deep learning required! Designed for programmers who may be new to deep learning, this book offers practical, hands-on experience, not just an abstract understanding of theory. You’ll start from the basics, and using PyTorch with real datasets, you’ll quickly progress from your first neural network to advanced architectures like convolutional neural networks (CNNs), transformers, diffusion models, and graph neural networks (GNNs). Each project can be run on your own hardware or in the cloud, with annotated code available on GitHub. You’ll build and train models to:&nbsp; <ul><li>Classify and analyze images, sequences, and time series</li><li>Generate and transform data with autoencoders, GANs (generative adversarial networks), and diffusion models</li><li>Process natural language with recurrent neural networks and transformers</li><li>Model molecules and physical systems with graph neural networks</li><li>Improve continuously through reinforcement and active learning</li><li>Predict chaotic systems with reservoir computing</li></ul> Whether you’re an engineer, scientist, or professional developer, you’ll gain fluency in deep learning and the confidence to apply it to ambitious, real-world problems. With Deep Learning Crash Course, you’ll move from using AI tools to creating them.

Deep Learning Crash Course

Advanced Studies in Theoretical and Applied Econometrics

Professor Robin C. Sickles has been an Associate Editor of Empirical Economics for 20 years (2000–2019). Originally featured as a Special Issue in Empirical Economics in 2025, this book gathers esteemed econometricians to honor Sickles's influential work. This Festschrift celebrates his distinguished service to the journal and his lifelong accomplishments in academics. Professor Robin C. Sickles has made important contributions to many areas of econometrics research, including modeling productivity and efficiency, health and crime, and several areas of applied and empirical economics. Robin is an applied econometrician who focuses on the role that econometrics plays in policy issues, such as market regulation, market transition, and deterrence versus preventive measures in the criminal justice system. This Festschrift has both theoretical and applied contributions in econometrics on topics related to Robin’s work. These include panel data, treatment effects, proxy variables, semiparametric stochastic frontier models, social security disability applications, the impact of operational efficiency (OE) measures on green innovation, a randomly swapped Bootstrap for paired data, estimating flexible functional forms using macroeconomic data, the impact of measurement error on trends in earnings inequality in the USA, racial and ethnic differences in quitting when marijuana retail markets are legal, a spatial stochastic frontier model with a stochastic weighting matrix, sectoral trade elasticities and regional productivity in Europe, efficiency of Queensland public hospitals, homeownership decisions among migrants in China, and peer effects with sample selection in online job training.

Professor Robin C. Sickles has been an Associate Editor of Empirical Economics for 20 years (2000–2019). Originally featured as a Special Issue in Empirical Economics in 2025, this book gathers esteemed econometricians to honor Sickles's influential work. This Festschrift celebrates his distinguished service to the journal and his lifelong accomplishments in academics. Professor Robin C. Sickles has made important contributions to many areas of econometrics research, including modeling productivity and efficiency, health and crime, and several areas of applied and empirical economics. Robin is an applied econometrician who focuses on the role that econometrics plays in policy issues, such as market regulation, market transition, and deterrence versus preventive measures in the criminal justice system. This Festschrift has both theoretical and applied contributions in econometrics on topics related to Robin’s work. These include panel data, treatment effects, proxy variables, semiparametric stochastic frontier models, social security disability applications, the impact of operational efficiency (OE) measures on green innovation, a randomly swapped Bootstrap for paired data, estimating flexible functional forms using macroeconomic data, the impact of measurement error on trends in earnings inequality in the USA, racial and ethnic differences in quitting when marijuana retail markets are legal, a spatial stochastic frontier model with a stochastic weighting matrix, sectoral trade elasticities and regional productivity in Europe, efficiency of Queensland public hospitals, homeownership decisions among migrants in China, and peer effects with sample selection in online job training.

A Festschrift in Honor of Robin C. Sickles

Productivity, Panel Data and Applied Econometrics

This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.

Starting with the fundamentals—LLM architecture, tokenization, APIs, and fine-tuning—the book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that complete multi-step tasks, and create real-world chatbots that understand and adapt to user needs. The approach is project-driven: each chapter includes visual explanations, step-by-step code walkthroughs, and deployment-ready examples. From building a personal assistant that searches your notes to creating a scheduling agent, every project reinforces both technical skills and applied understanding. It emphasizes clarity, inclusivity, and real-world relevance—helping readers move confidently from basic understanding to complex applications.

Whether you're exploring Agentic AI or looking to build production-ready systems, this book gives you the tools to turn curiosity into capability—and innovation into impact.

What you will learn:

Build intelligent chatbots and tools using LLMs like GPT, LLaMA, and Mistral with guided development steps.

Combine LLMs with vector databases like FAISS and Pinecone to create accurate, context-aware AI systems.

Design AI agents capable of planning and executing complex workflows for automation and decision-making.

Apply prompt engineering, memory, and multimodal tools to build real-world AI apps for your project portfolio.




Who this book is for:

Machine Learning engineers, data scientists, and AI professionals interested in learning how to build real-world AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and intelligent chatbots.

This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.Starting with the fundamentals—LLM architecture, tokenization, APIs, and fine-tuning—the book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that complete multi-step tasks, and create real-world chatbots that understand and adapt to user needs. The approach is project-driven: each chapter includes visual explanations, step-by-step code walkthroughs, and deployment-ready examples. From building a personal assistant that searches your notes to creating a scheduling agent, every project reinforces both technical skills and applied understanding. It emphasizes clarity, inclusivity, and real-world relevance—helping readers move confidently from basic understanding to complex applications.Whether you're exploring Agentic AI or looking to build production-ready systems, this book gives you the tools to turn curiosity into capability—and innovation into impact.What you will learn:<ul><li>Build intelligent chatbots and tools using open-source LLMs like GPT, LLaMA, and Mistral with guided deployment steps.</li><li>Combine LLMs with vector databases like FAISS and Pinecone to create accurate, context-aware AI systems.</li><li>Design AI agents capable of planning and executing complex workflows for automation and decision-making.</li><li>Apply prompt engineering, memory, and multimodal tools to build real-world AI apps for your project portfolio.</li></ul>Who this book is for:Machine Learning engineers, data scientists, and AI professionals interested in learning how to build real-world AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and intelligent chatbots.

Architectures, Applications, and Real-World Deployments of Large Language Models

Mastering Large Language Models

Mastering Sector and Industry Analysis

The Art of Financial Terrain

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.
Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.
You Will:
<ul>
<li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li>
<li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li>
<li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li>
</ul>
&nbsp;
This book is for : Software Architects &amp; System Designers
&nbsp;

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.You Will:<ul><li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li><li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li><li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li></ul>&nbsp;This book is for : Software Architects &amp; System Designers&nbsp;

Architecting Scalable, Ethical, and Trustworthy Systems for Enterprise

Agentic AI Systems in Practice

Mit diesem Natur-Pappebuch entdecken kleine und große Forscher*innen den faszinierenden Kreislauf des Lebens von Tieren und Pflanzen. Auf jeder Doppelseite lädt ein drehbares Rad zum Mitmachen ein und zeigt anschaulich, wie alles miteinander verbunden ist. Vielfältige Klappen sorgen für zusätzlichen Spaß und motivieren die Kinder dazu, selbst aktiv zu werden. Die liebevoll geschriebenen Texte geben wertvolle Tipps, wie man Tiere und Pflanzen in der Natur beobachten und schützen kann, und bringen so die Vielfalt der Natur spielerisch näher.

Mit Klappen und Drehrad – Spannende Einblicke in die Wunder der Natur | Sach-Pappebuch für Kinder ab 3 Jahren

Was wächst da?

This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments.
This book guides through the entire modern machine learning lifecycle. You’ll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You’ll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you’ll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you’ll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints.
In the end, this book helps you build systems that are robust, auditable, and optimized—whether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era.
What you will learn:
Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples.
Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques.
Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks.
Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments.
Who this book is for:
This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale.

This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments.This book guides through the entire modern machine learning lifecycle. You’ll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You’ll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you’ll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you’ll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints.In the end, this book helps you build systems that are robust, auditable, and optimized—whether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era.What you will learn:Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples.Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques.Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks.Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments.Who this book is for:This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale.&nbsp;

Practical Machine Learning and Agentic Workflows with Python and PyTorch

Building AI Systems with Python

The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.
Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.
The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.
Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today.
 
What you will learn:
<ul>
<li>Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework 

</li><li>Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation 

</li><li>Build and optimize RAG pipelines with effective retrieval strategies and vector databases 

</li><li>Deploy optimized LLMs using quantization techniques and scalable inference frameworks 

</li><li>Develop multimodal and agentic AI applications with vision-language models and autonomous agents 



 
Who this book is for:

This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start
</li></ul>

The Large Language Model Recipes book 
 is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.
 Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today.&nbsp;What you will learn:<ul><li>Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework </li><li>Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation </li><li>Build and optimize RAG pipelines with effective retrieval strategies and vector databases </li><li>Deploy optimized LLMs using quantization techniques and scalable inference frameworks </li><li>Develop multimodal and agentic AI applications with vision-language models and autonomous agents </li></ul>&nbsp;Who this book is for:This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start

A Hands-On Guide to Fine-Tuning, Optimization, Deployment, and Real-World Applications

Large Language Model Recipes

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