AI in Production: Governance, Reliability, and Application by Manning

Get the eBook free when you register your print book at Manning. Build AI Drug Discovery Pipelines introduces the machine learning and deep learning techniques that drive modern medical research. Each chapter covers a real-world example from the pharmaceutical industry, showing you hands on how researchers investigate treatments for cancer, malaria, autoimmune diseases, and more. You'll even explore the techniques used to create Deepmind's Alphafold, in an in-depth case study of the groundbreaking model. Machine learning has accelerated the process of drug discovery, shortening the timeline for developing new medicines from decades to years or months. In this practical guide, you’ll learn to create the kind of machine learning models that make these discoveries possible. You'll work with a full implementation of the Alphafold model created by Google Deepmind and Nobel Prize Winner Sir Demis Hassabis, examine Nvidia's BioNeMo pipeline, and explore other industry models. In Build AI Drug Discovery Pipelines you will learn: • Drug discovery and virtual screening • Classic ML, deep learning, and LLMs for drug discovery • Using RDKit to analyze molecular data • Creating drug discovery models with PyTorch • Replicating cutting-edge drug development research About the book Build AI Drug Discovery Pipelines introduces the fundamentals of drug discovery and cheminformatics along with the machine learning techniques used by leaders in the pharmaceutical industry. Each chapter guides you through an engaging hands-on project that explores a real medical issue. You’ll build a full screening pipeline to assess a compound’s potential for treating malaria, reproduce published methods for HIV drug design, learn to use deep generative models for novel drug optimization, and see how LLMs can overcome common problems of protein folding. About the reader All you need are the basics of Python. This book will teach you everything else. About the author Noah Flynn is a research scientist at Amazon with a PhD in Computational Biology from Washington University in St. Louis. He has developed deep learning applications to screen drugs for bioactivation, reactive metabolite formation, drug-drug interactions, and other types of toxicity problems. He has worked at AbbVie and Merck on analysis of gene regulatory networks and protein-protein interactions and applications of generative models to construct and optimize novel compound libraries. He now researches applications of large language models at Amazon.

Get the eBook free when you register your print book at Manning. This book is a fast, friendly guide to integrating large language models into your data workflows. You’ll learn how AI can help you handle time-consuming data engineering tasks including transformations, calculations, and the never-ending chore of data cleaning–all illustrated with instantly-familiar SQL and Python use cases! This book shows you far more than copy-pasting prompts or using AI as a coding assistant. You’ll learn how to integrate LLMs at an API level to give a real performance and efficiency boost to your pipelines. Each chapter contains a new relevant technique to automate transformations, enrich datasets, and accelerate tedious workflows with AI. In Learn AI Data Engineering you’ll learn how to: • Craft smarter prompts to get the best results from LLMs on data tasks • Use ChatGPT to write, debug, and optimize SQL and Python faster • Embed AI directly into pipelines for code automation, analysis, and enrichment • Clean, transform, and extract insights from real-world messy data with AI • Build agentic workflows that capture and scale subject matter expertise About the book Learn AI Data Engineering teaches you to apply AI to the most important data engineering tasks. You’ll build a portfolio of hands-on projects, like querying the Pagila DVD rental database with ChatGPT, performing real-time sentiment analysis on news headlines, and even prototyping agentic workflows that simulate real business rules. With each lesson you’ll learn just enough theory to understand the why, and then immediately put it into practice. About the reader For aspiring and veteran data professionals who know Python and SQL. About the author Dave Melillo is a data leader with deep expertise across data analytics, data engineering, data architecture, and data science and machine learning. He has built large-scale data products, architected modern pipelines, and trained professionals at universities and Fortune 500 companies. His writing on practical data strategies has been featured in Towards Data Science and Dev Genius.

Understand the architecture that underpins today’s most powerful AI models. Transformers are the superpower behind large language models (LLMs) like ChatGPT, Gemini, and Claude. Transformers in Action gives you the insights, practical techniques, and extensive code samples you need to adapt pretrained transformer models to new and exciting tasks. Inside Transformers in Action you’ll learn: • How transformers and LLMs work • Modeling families and architecture variants • Efficient and specialized large language models • Adapt HuggingFace models to new tasks • Automate hyperparameter search with Ray Tune and Optuna • Optimize LLM model performance • Advanced prompting and zero/few-shot learning • Text generation with reinforcement learning • Responsible LLMs Transformers in Action takes you from the origins of transformers all the way to fine-tuning an LLM for your own projects. Author Nicole Koenigstein demonstrates the vital mathematical and theoretical background of the transformer architecture practically through executable Jupyter notebooks. You’ll discover advice on prompt engineering, as well as proven-and-tested methods for optimizing and tuning large language models. Plus, you’ll find unique coverage of AI ethics, specialized smaller models, and the decoder encoder architecture. Foreword by Luis Serrano. About the technology Transformers are the beating heart of large language models (LLMs) and other generative AI tools. These powerful neural networks use a mechanism called self-attention, which enables them to dynamically evaluate the relevance of each input element in context. Transformer-based models can understand and generate natural language, translate between languages, summarize text, and even write code—all with impressive fluency and coherence. About the book Transformers in Action introduces you to transformers and large language models with careful attention to their design and mathematical underpinnings. You’ll learn why architecture matters for speed, scale, and retrieval as you explore applications including RAG and multi-modal models. Along the way, you’ll discover how to optimize training and performance using advanced sampling and decoding techniques, use reinforcement learning to align models with human preferences, and more. The hands-on Jupyter notebooks and real-world examples ensure you’ll see transformers in action as you go. What's inside • Optimizing LLM model performance • Adapting HuggingFace models to new tasks • How transformers and LLMs work under the hood • Mitigating bias and responsible ethics in LLMs About the reader For data scientists and machine learning engineers. About the author Nicole Koenigstein is the Co-Founder and Chief AI Officer at the fintech company Quantmate. Table of Contents Part 1 1 The need for transformers 2 A deeper look into transformers Part 2 3 Model families and architecture variants 4 Text generation strategies and prompting techniques 5 Preference alignment and retrieval-augmented generation Part 3 6 Multimodal models 7 Efficient and specialized small language models 8 Training and evaluating large language models 9 Optimizing and scaling large language models 10 Ethical and responsible large language models

Automate and accelerate your everyday IT tasks with instant solutions! What if you never had to write another after-incident report, piece of boilerplate code, or a performance review from scratch ever again? Use AI tools like ChatGPT, Claude, Gemini, and Copilot right, and you’ll take back hours of your time—and more! AI for Everyday IT reveals how you can automate dozens of your daily IT tasks with generative AI. In AI for Everyday IT you’ll learn how to: • Write effective prompts for common IT tasks • Optimize report generation, document handling, and workplace communication • Resolve IT conflicts and crises • Acquire new skills and upgrade your resume • AI for help desk, database administration and systems administration • Incorporate AI into DevOps processes and create AI-powered applications • Simplify time-consuming people management tasks In this hands-on guide, automation experts Chrissy LeMaire and Brandon Abshire show you how AI tools like ChatGPT have made their lives a million times easier, and how they can do the same for you. You’ll find proven strategies for using AI to improve help desk support, automate sysadmin and database tasks, aid with DevOps engineering, handle managing IT teams, and dozens more time-saving and quality-improving hacks. Foreword by Nitya Narasimhan. About the technology Have you lost days sifting through logs to find a latency issue? AI can do it in seconds! Need to update your documentation? Mere moments for AI. Are you writing scripts, designing data recovery strategies, and evaluating network designs? AI can handle it all—if you know how to use it. About the book AI for Everyday IT shows you exactly how AI can transform support desk operations, root cause analysis, disaster recovery planning—even writing professional emails when you’re too furious to be nice! This instantly-useful guide has time-saving techniques for all IT pros—from devs and DBAs to technical writers and product managers. Each relatable example is fully illustrated with the prompts and problem formulation strategies, along with interesting insights and anecdotes from authors Chrissy Lemaire and Brandon Abshire. What's inside • Document handling and workplace communication • Database administration and development • DevOps engineering and AI powered apps • People management and career planning About the reader Whether you’re working in operations, development, management, or security, you’ll love these productivity hacks for generative AI. No previous AI experience required. About the author Chrissy LeMaire is a dual Microsoft MVP and GitHub Star, the creator of dbatools, and author of the Manning book Learn dbatools in a Month of Lunches. Brandon Abshire has spent over twenty years in IT, including roles at a leading Fortune 500 semiconductor and telecommunications company and multiple top-ranked US hospital systems.

Solve design, planning, and control problems using modern AI techniques. Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn: • The core concepts of search and optimization • Deterministic and stochastic optimization techniques • Graph search algorithms • Trajectory-based optimization algorithms • Evolutionary computing algorithms • Swarm intelligence algorithms • Machine learning methods for search and optimization problems • Efficient trade-offs between search space exploration and exploitation • State-of-the-art Python libraries for search and optimization Inside this comprehensive guide, you’ll find a wide range of optimization methods, from deterministic search algorithms to stochastic derivative-free metaheuristic algorithms and machine learning methods. Don’t worry—there’s no complex mathematical notation. You’ll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world. Plus, get hands-on experience with practical exercises to optimize and scale the performance of each algorithm. About the technology Every time you call for a rideshare, order food delivery, book a flight, or schedule a hospital appointment, an algorithm works behind the scenes to find the optimal result. Blending modern AI methods with classical search and optimization techniques can deliver incredible results, especially for the messy problems you encounter in the real world. This book shows you how. About the book Optimization Algorithms explains in clear language how optimization algorithms work and what you can do with them. This engaging book goes beyond toy examples, presenting detailed scenarios that use actual industry data and cutting-edge AI techniques. You will learn how to apply modern optimization algorithms to real-world problems like pricing products, matching supply with demand, balancing assembly lines, tuning parameters, coordinating mobile networks, and cracking smart mobility challenges. What's inside • Graph search algorithms • Metaheuristic algorithms • Machine learning methods • State-of-the-art Python libraries for optimization • Efficient trade-offs between search space exploration and exploitation About the reader Requires intermediate Python and machine learning skills. About the author Dr. Alaa Khamis is an AI and smart mobility technical leader at General Motors and a lecturer at the University of Toronto. The technical editor on this book was Frances Buontempo. Table of Contents PART 1 1 Introduction to search and optimization 2 A deeper look at search and optimization 3 Blind search algorithms 4 Informed search algorithms PART 2 5 Simulated annealing 6 Tabu search PART 3 7 Genetic algorithms 8 Genetic algorithm variants PART 4 9 Particle swarm optimization 10 Other swarm intelligence algorithms to explore PART 5 11 Supervised and unsupervised learning 12 Reinforcement learning Appendix A Appendix B Appendix C

Build working and regulation-compliant financial software—from scratch! The software used by banks, trading firms, and other financial services has special requirements at every level, from securing the UI to making sure backend services comply with a host of regulations. Build Financial Software with Generative AI (From Scratch) shows you how to deliver full stack financial services software—and how generative AI can make you even more productive. In Build Financial Software with Generative AI (From Scratch) you will: • Explore the core concepts of FinTech • Speed development with generative AI tools • Develop and deploy containerized services • Create and document APIs • Effectively visualize your data In Build Financial Software with Generative AI (From Scratch) you’ll build working software for processing Automated Clearing House (ACH) files, a cornerstone technology of banking that moves trillions of dollars every year. You’ll work with generative AI technology throughout the full stack application, including researching the tech for your application, spinning up a bare bone starting project, answering domain questions, clarifying functionality, and troubleshooting. Along the way, you’ll learn what sets FinTech projects apart from normal web apps. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology The financial industry is awash with regulatory and compliance challenges, complex technical requirements, and stringent security demands. There’s a huge demand for developers who can create financial services software and this book will get you started. You’ll build your own FinTech app from the ground up—with a big productivity boost from Generative AI! About the book Build Financial Software with Generative AI (From Scratch) guides you through modernizing a full-stack Automated Clearing House (ACH) application, layer-by-layer. You’ll start with a quick review of FinTech basics and an introduction to GenAI tools. Then, you’ll develop a data visualization dashboard with React, containerize components with Docker, create and refine APIs, implement backend processing, and even design a custom database. Throughout, you’ll see how AI tools aid with coding, testing, research, security, documentation, and even Agile practices. What's inside • Learn the core concepts of FinTech development • Create and document APIs using Generative AI • Build an awesome data visualization dashboard About the reader Examples are in Python. No experience with generative AI or financial services required. About the author Christopher Kardell and Mark Brouwer have both spent more than 20 years working in the Fintech industry. Table of Contents Part 1 1 Exploring FinTech and generative AI 2 Parsing payments Part 2 3 Getting started with Docker 4 APIs: The piping between our components 5 Storing our ACH files 6 Taking the next step with Next.js 7 Our minimum viable product Part 3 8 Exceptions 9 Searching and auditing 10 Company information 11 International ACH transactions and OFAC scanning 12 Where to go from here

Delivering a successful machine learning project is hard. This book makes it easier. In it, you’ll design a reliable ML system from the ground up, incorporating MLOps and DevOps along with a stack of proven infrastructure tools including Kubeflow, MLFlow, BentoML, Evidently, and Feast. A properly designed machine learning system streamlines data workflows, improves collaboration between data and operations teams, and provides much-needed structure for both training and deployment. In this book you’ll learn how to design and implement a machine learning system from the ground up. You’ll appreciate this instantly-useful introduction to achieving the full benefits of automated ML infrastructure. In Machine Learning Platform Engineering you’ll learn how to: • Set up an MLOps platform • Deploy machine learning models to production • Build end-to-end data pipelines • Effective monitoring and explainability About the technology AI and ML systems have a lot of moving parts, from language libraries and application frameworks, to workflow and deployment infrastructure, to LLMs and other advanced models. A well-designed internal development platform (IDP) gives developers a defined set of tools and guidelines that accelerate the dev process, improving consistency, security, and developer experience. About the book Machine Learning Platform Engineering shows you how to build an effective IDP for ML and AI applications. Each chapter illuminates a vital part of the ML workflow, including setting up orchestration pipelines, selecting models, allocating resources for training, inference, and serving, and more. As you go, you’ll create a versatile modern platform using open source tools like Kubeflow, MLFlow, BentoML, Evidently, Feast, and LangChain. What's inside • Set up an end-to-end MLOps/LLMOps platform • Deploy ML and AI models to production • Effective monitoring, evaluation, and explainability About the reader For data scientists or software engineers. Examples in Python. About the author Benjamin Tan Wei Hao leads a team of ML engineers and data scientists at DKatalis. Shanoop Padmanabhan is a software engineering manager at Continental Automotive. Varun Mallya is a senior ML engineer at DKatalis. Table of Contents Part 1 1 Getting started with MLOps and ML engineering 2 What is MLOps? 3 Building applications on Kubernetes Part 2 4 Designing reliable ML systems 5 Orchestrating ML pipelines 6 Productionizing ML models Part 3 7 Data analysis and preparation 8 Model training and validation: Part 1 9 Model training and validation: Part 2 10 Model inference and serving 11 Monitoring and explainability Part 4 12 Designing LLM-powered systems 13 Production LLM system design A Installation and setup B Basics of YAML

Practical techniques to accelerate software development using generative AI. Let’s get real. You’d like to hand off a lot of tedious software development tasks to an assistant—and now you can! AI-powered coding tools like Copilot can accelerate research, design, code creation, testing, troubleshooting, documentation, refactoring and more. Coding with AI shows you how. Written for working developers, this book fast-tracks you to AI-powered productivity with bite-size projects, tested prompts, and techniques for getting the most out of AI. In Coding with AI you’ll learn how to: • Incorporate AI tools into your development workflow • Create pro-quality documentation and tests • Debug and refactor software efficiently • Create and organize reusable prompts Coding with AI takes you through several small Python projects with the help of AI tools, showing you exactly how to use AI to create and refine real software. This book skips the baby steps and goes straight to the techniques you’ll use on the job, every day. You’ll learn to sidestep AI inefficiencies like hallucination and identify the places where AI can save you the most time and effort. Unique tips, tricks, and prompt hacks ensure you’re getting the best out of AI. About the book Coding with AI is a deep dive into AI coding tools, quickly getting you up to speed with effective code generation in a professional development environment. You’ll benefit from author Jeremy Morgan’s clear-eyed developer-to-developer approach based on his real-world experience of AI coding. Hands-on Python projects demonstrate how you can use AI to organize prompts, create documentation, debug your software, and more. About the reader For professional software developers. Examples in Python. About the author Jeremy Morgan is on a mission to help developers get better at what they do. He's a Senior Training Architect for KodeKloud and an avid tech blogger and speaker. He has two decades of experience as an engineer building software for everything from Fortune 100 companies to tiny startups. Jeremy hosts the popular tech blog, www.jeremymorgan.com. He also contributes to open source as a .NET Foundation Member, DevOps Institute Ambassador, and serves on a DevNetwork advisory board. Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Speed up your testing and deliver exceptional product quality with the power of AI tools. The more you test, the more you learn about your software. Software Testing with Generative AI shows you how you can expand, automate, and enhance your testing with Large Language Model (LLM)-based AI. Your team will soon be delivering higher quality tests, all in less time. In Software Testing with Generative AI you’ll learn how to: • Spot opportunities to improve test quality with AI • Construct test automation with the support of AI tools • Formulate new ideas during exploratory testing using AI tools • Use AI tools to aid the design process of new features • Improve the testability of a context with the help of AI tools • Maximize your output with prompt engineering • Create custom LLMs for your business’s specific needs Software Testing with Generative AI is full of hype-free advice for supporting your software testing with AI. In it, you’ll find strategies from bestselling author Mark Winteringham to generate synthetic testing data, implement automation, and even augment and improve your test design with AI. Foreword by Nicola Martin. About the technology There’s a simple rule in software testing: the more you test, the more you learn. And as any testing pro will tell you, good testing takes time. By integrating large language models (LLMs) and generative AI into your process, you can dramatically automate and enhance testing, improve quality and coverage, and deliver more meaningful results. About the book Software Testing with Generative AI shows you how AI can elevate every aspect of testing—automation, test data management, test scripting, exploratory testing, and more! Learn how to use AI coding tools like Copilot to guide test-driven development, get relevant feedback about your applications from ChatGPT, and use the OpenAI API to integrate AI into your data generation. You’ll soon have higher-quality testing that takes up less of your time. What's inside • Improve test quality and coverage • AI-powered test automation • Build agents that act as testing assistants About the reader For developers, testers, and quality engineers. About the author Mark Winteringham is an experienced software tester who teaches many aspects of software testing. He is the author of Testing Web APIs. The technical editor on this book was Robert Walsh. Table of Contents Part 1 1 Enhancing testing with large language models 2 Large language models and prompt engineering 3 Artificial intelligence, automation, and testing Part 2 4 AI-assisted testing for developers 5 Test planning with AI support 6 Rapid data creation using AI 7 Accelerating and improving UI automation using AI 8 Assisting exploratory testing with artificial intelligence 9 AI agents as testing assistants Part 3 10 Introducing customized LLMs 11 Contextualizing prompts with retrieval-augmented generation 12 Fine-tuning LLMs with business domain knowledge Appendix A Setting up and using ChatGPT Appendix B Setting up and using GitHub Copilot Appendix C Exploratory testing notes

Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action! Generative AI in Action is the comprehensive and concrete guide to generative AI you’ve been searching for. It introduces both AI’s fundamental principles and its practical applications in an enterprise context—from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. Inside Generative AI in Action you will find: • A practical overview of of generative AI applications • Architectural patterns, integration guidance, and best practices for generative AI • The latest techniques like RAG, prompt engineering, and multi-modality • The challenges and risks of generative AI like hallucinations and jailbreaks • How to integrate generative AI into your business and IT strategy Generative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You’ll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale. About the technology In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading! About the book Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you’ll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You’ll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems. What's inside • Best practices for deploying Generative AI apps • Production-quality RAG • Adapting GenAI models to your specific domain About the reader For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI. About the author Amit Bahree is Principal Group Product Manager for the Azure AI engineering team at Microsoft. The technical editor on this book was Wee Hyong Tok. Table of Contents Part 1 1 Introduction to generative AI 2 Introduction to large language models 3 Working through an API: Generating text 4 From pixels to pictures: Generating images 5 What else can AI generate? Part 2 6 Guide to prompt engineering 7 Retrieval-augmented generation: The secret weapon 8 Chatting with your data 9 Tailoring models with model adaptation and fine-tuning Part 3 10 Application architecture for generative AI apps 11 Scaling up: Best practices for production deployment 12 Evaluations and benchmarks 13 Guide to ethical GenAI: Principles, practices, and pitfalls A The book’s GitHub repository B Responsible AI tools

“Looks Good to Me” teaches the considerate, common sense approach to code reviews pioneered by author Adrienne Braganza. You’ll learn how to create a cohesive team environment, align review goals and expectations clearly, and be prepared for any changes or obstacles you may face. Along the way, you’ll master practices that adapt to how your team does things, with multiple options and solutions, relatable scenarios, and personal tidbits. You’ll soon be running highly effective reviews that make your code—and your team—stronger.

De-risk AI models, validate real-world performance, and align output with product goals. Before you trust critical business systems to an AI model, you need to answer a few questions. Will it be fast enough? Will the system satisfy user expectations? Is it safe? Can you trust the output? This book will help you answer these questions and more before you roll out an AI system—and make sure it runs smoothly after you deploy. In AI Model Evaluation you’ll learn how to: • Build diagnostic offline evaluations that uncover model behavior • Use shadow traffic to simulate production conditions • Design A/B tests that validate model impact on key product metrics • Spot nuanced failures with human-in-the-loop feedback • Use LLMs as automated judges to scale your evaluation pipeline In AI Model Evaluation author Leemay Nassery shares her hard-won experiences specializing in experimentation and personalization across companies such as Spotify, Comcast, Dropbox, and Etsy. The book is packed with insights on what it really takes to get a model ready for production. You’ll go beyond basic performance evaluations to discover how you can measure model effectiveness on the product, spot latency issues as you introduce the model in your end-to-end architecture, and understand the model’s real-world impact. About the book AI Model Evaluation teaches you how to effectively evaluate and assess machine learning models for better scaling and integration into production systems. Each chapter tackles a different evaluation method. You'll start with offline evaluations, then move into live A/B tests, shadow traffic deployments, qualitative evaluations, and LLM-based feedback loops. You’ll learn how to evaluate both model behavior and engineering system performance, with a hands-on example grounded in a movie recommendation engine. About the reader For practitioners with experience in machine learning, data science, or software engineering. Familiarity with Python is recommended. About the author Leemay Nassery is an engineering leader specializing in experimentation and personalization. With a notable track record that includes evolving Spotify's A/B testing strategy for the Homepage, launching Comcast's For You page, and establishing data warehousing teams at Etsy, she firmly believes that the key to innovation at any company is the ability to experiment effectively.

Create and improve conversational AI with the latest patterns, best practices, and tools, including generative AI models. Conversational AI (CAI) tools are built to solve problems, but all-too-often they just end up causing pain for users–and developers! Effective Conversational AI reveals best practices and industry-tested techniques for creating chatbots and conversational AI tools that are reliable at an enterprise scale. With the tested ideas and examples in this book, you’ll learn to build chatbots that your customers and colleagues will actually want to use! In Effective Conversational AI you’ll learn how to: • Create high-quality chatbots and other conversational AI experiences • Plan for continuous improvement • Incorporate generative AI solutions to improve quality, accuracy, and usability • Evaluate user experience and business results Effective Conversational AI introduces continuous improvement practices that are vital for the constant betterment and evolution of chatbots and CAI tools. It introduces the three most-common forms of chatbot—Q&A, process-oriented, and routing agents—and presents a reliable framework for continuously improving each one. Using modern generative AI and tried-and-tested classic approaches, you’ll learn to deliver high performance chatbots that can guide a customer through complex end-to-end tasks—no human required! Foreword by Jesús Mantas. About the technology Powerful new chatbot frameworks and Generative AI models can practically eliminate problems like misinterpreting user intent and delivering nonsensical answers. In this book, you’ll learn how to build chatbots that take advantage of large language models and other modern tools and create conversational AI experiences users will love. About the book Effective Conversational AI teaches you how to build great chatbots that perform reliably even at enterprise scale. In it, you’ll learn how to clarify user intent using LLMs, respond accurately to unanticipated input, and use Retrieval Augmented Generation to keep responses up to date. Along the way, you’ll discover how to establish a feedback loop for continuous quality improvement and master techniques to integrate GenAI safely into conventional chatbot designs. What's inside • Blend Generative AI and conventional chatbot tools • Use LLMs to improve quality, accuracy, and usability • Plan for continuous improvement • Domain-specific responses using RAG About the reader For developers, engineers, and product managers working with conversational AI. About the author Andrew Freed, Cari Jacobs, and Eniko Rózsa are seasoned conversational AI developers with IBM. Table of Contents Part 1 1 What makes conversational AI work? 2 Building a conversational AI 3 Planning for improvement Part 2 4 Understanding what your users really want 5 Improving weak understanding for traditional AI 6 Enhancing responses with retrieval-augmented generation 7 Augmenting intent data with generative AI Part 3 8 Streamlining complex flows 9 Harnessing context for an adaptive virtual assistant experience 10 Reducing complexity with generative AI Part 4 11 Reducing opt-outs 12 Conversational summarization for smooth handoff

Use Spring AI to add generative AI features like virtual assistants, text summaries, and suggestions to your Java applications.Generative AI tools like ChatGPT cause an immediate jaw drop for almost everyone who encounters them.Until recently, though, Java developers have had few good tools for adding AI features to existing and new applications.

Apply cutting-edge machine learning techniques—from crowdsourced relevance and knowledge graph learning, to Large Language Models (LLMs)—to enhance the accuracy and relevance of your search results. Delivering effective search is one of the biggest challenges you can face as an engineer. AI-Powered Search is an in-depth guide to building intelligent search systems you can be proud of. It covers the critical tools you need to automate ongoing relevance improvements within your search applications. Inside you’ll learn modern, data-science-driven search techniques like: • Semantic search using dense vector embeddings from foundation models • Retrieval augmented generation (RAG) • Question answering and summarization combining search and LLMs • Fine-tuning transformer-based LLMs • Personalized search based on user signals and vector embeddings • Collecting user behavioral signals and building signals boosting models • Semantic knowledge graphs for domain-specific learning • Semantic query parsing, query-sense disambiguation, and query intent classification • Implementing machine-learned ranking models (Learning to Rank) • Building click models to automate machine-learned ranking • Generative search, hybrid search, multimodal search, and the search frontier AI-Powered Search will help you build the kind of highly intelligent search applications demanded by modern users. Whether you’re enhancing your existing search engine or building from scratch, you’ll learn how to deliver an AI-powered service that can continuously learn from every content update, user interaction, and the hidden semantic relationships in your content. You’ll learn both how to enhance your AI systems with search and how to integrate large language models (LLMs) and other foundation models to massively accelerate the capabilities of your search technology. Foreword by Grant Ingersoll. About the technology Modern search is more than keyword matching. Much, much more. Search that learns from user interactions, interprets intent, and takes advantage of AI tools like large language models (LLMs) can deliver highly targeted and relevant results. This book shows you how to up your search game using state-of-the-art AI algorithms, techniques, and tools. About the book AI-Powered Search teaches you to create a search that understands natural language and improves automatically the more it is used. As you work through dozens of interesting and relevant examples, you’ll learn powerful AI-based techniques like semantic search on embeddings, question answering powered by LLMs, real-time personalization, and Retrieval Augmented Generation (RAG). What's inside • Sparse lexical and embedding-based semantic search • Question answering, RAG, and summarization using LLMs • Personalized search and signals boosting models • Learning to Rank, multimodal, and hybrid search About the reader For software developers and data scientists familiar with the basics of search engine technology. About the author Trey Grainger is the Founder of Searchkernel and former Chief Algorithms Officer and SVP of Engineering at Lucidworks. Doug Turnbull is a Principal Engineer at Reddit and former Staff Relevance Engineer at Spotify. Max Irwin is the Founder of Max.io and former Managing Consultant at OpenSource Connections.

Build the kind of software engineering platforms developers love to use! An effective developer platform means one thing: productivity. Effective Platform Engineering introduces platform engineering as a discipline, teaching you what goes into a great developer platform and then showing you how to develop your own. In Effective Platform Engineering, you’ll learn to deliver: • Improved business outcomes • Product management strategies • Secure, scalable Kubernetes-based engineering platforms • Effective Service Level Objectives to boost trust and adoption • Modern control plane patterns that streamline developer workflows Effective Platform Engineering shows you how to establish and administer the kind of platforms that reduce a dev team’s workload, improve consistency, and accelerate software delivery. Organized in three parts—foundations, building, and scaling—it blends mental models, patterns, and runnable examples to help you go from MVP to production-grade platform. You’ll learn how to treat platforms as products and demonstrate their value by improving engineering outcomes and delivering measurable business impact. You’ll love the book’s informed guidance and practical insights on satisfying stakeholders and driving organizational success. Foreword by Kief Morris. About the technology Platform engineering is the discipline of building internal developer platforms (IDPs) that automate and simplify the software development lifecycle. IDPs provide self-service interfaces to tools and services for infrastructure, deployment, security, observability, and governance, and reduce the need for developers to master complex technologies like Kubernetes. Because they match the specific needs of an organization, well-engineered IDPs can reduce development time by half while delivering better software. About the book With Effective Platform Engineering you’ll be able to design, build, deploy, and scale a custom IDP. You’ll discover how platform engineering differs from DevOps and learn to create a platform that evolves with changing requirements. Diagrams, code samples, and exercises help you visualize important concepts. Along the way, you’ll explore critical topics like gathering requirements and establishing realistic service level agreements (SLAs). What's inside • Automated application workflows • Scalable Kubernetes-based platforms • A well-designed control plane • Custom APIs About the reader For software engineers, SREs, and DevOps engineers. About the author Ajay Chankramath is the Founder and CEO of Platformetrics. Nic Cheneweth and Bryan Oliver are Principal Consultants at Thoughtworks. Sean Alvarez is the CTO of Life Sciences at Brillio. Table of Contents Part 1 1 What is platform engineering? 2 Software-defined products and architectures 3 Measuring your way to platform engineering success Part 2 4 Governance, compliance, and trust 5 Evolutionary observability 6 Building a software-defined engineering platform 7 Platform control plane foundations 8 Control plane services and extensions Part 3 9 Architecture changes to support scale 10 Platform product evolution A Solutions to the exercises

Separate the AI facts from the AI fiction, and discover how you can best put these tools to work in your organization. In Inside AI AI professor and entrepreneur Dr. Akli Adjaoute puts AI in perspective, with informed insights from 30 years spent in the field. His book lays out a pragmatic blueprint that every leader can utilize to drive innovation with artificial intelligence. In Inside AI you’ll learn how to: Gain insight into diverse AI techniques and methodologies Learn from both successful and failed AI applications Identify the capabilities and limitations of AI systems Understand successful and failed uses of AI in business See where human cognition still exceeds AI Bust common myths like AI’s threat to jobs and civilization Manage AI projects effectively Inside AI takes you on a journey through artificial intelligence, from AI’s origins in traditional expert systems all the way to deep learning and Large Language Models. There’s no hype here—you’ll get the grounded, evidence-based insights that are vital for making strategic decisions and preparing your business for the future. About the technology Artificial Intelligence enthusiasts promise everything from human-like collaboration on everyday tasks to the end of work as we know it. Is AI just a flash in the pan, or can it really transform how you do business? This intriguing book sifts through the hype and separates the truth from the myths, with clear advice on what AI can—and can’t—achieve. About the book Inside AI provides a clear-headed overview of modern artificial intelligence, including the recent advances of Generative AI and Large Language Models. Its accessible and jargon-free explanations of leading AI techniques showcase how AI delivers tangible advantages to businesses. Both inspiring and practical, this book provides a proven framework for developing successful AI applications. What's inside Insights from successful and failed AI applications A survey of AI techniques and methodologies Bust common AI myths Manage AI projects effectively About the reader For anyone seeking grounded insights into AI’s capabilities, including business leaders and decision makers. About the author Akli Adjaoute is the founder of multiple AI-related companies. He served as an adjunct professor at the University of San Francisco and as Scientific Committee Chair and Head of the AI department at EPITA. The technical editor on this book was Richard Vaughan. Table of contents 1 The rise of machine intelligence 2 AI mastery: Essential techniques, Part 1 3 AI mastery: Essential techniques, Part 2 4 Smart agent technology 5 Generative AI and large language models 6 Human vs. machine 7 AI doesn’t turn data into intelligence 8 AI doesn’t threaten our jobs 9 Technological singularity is absurd 10 Learning from successful and failed applications of AI 11 Next-generation AI A Tracing the roots: From mechanical calculators to digital dreams B Algorithms and programming languages

Summary Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Succeeding with AI sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Succeeding with AI requires talent, tools, and money. So why do many well-funded, state-of-the-art projects fail to deliver meaningful business value? Because talent, tools, and money aren’t enough: You also need to know how to ask the right questions. In this unique book, AI consultant Veljko Krunic reveals a tested process to start AI projects right, so you’ll get the results you want. About the book Succeeding with AI sets out a framework for planning and running cost-effective, reliable AI projects that produce real business results. This practical guide reveals secrets forged during the author’s experience with dozens of startups, established businesses, and Fortune 500 giants that will help you establish meaningful, achievable goals. In it you’ll master a repeatable process to maximize the return on data-scientist hours and learn to implement effectiveness metrics for keeping projects on track and resistant to calcification. What's inside Where to invest for maximum payoff How AI projects are different from other software projects Catching early warnings in time to correct course Exercises and examples based on real-world business dilemmas About the reader For project and business leadership, result-focused data scientists, and engineering teams. No AI knowledge required. About the author Veljko Krunic is a data science consultant, has a computer science PhD, and is a certified Six Sigma Master Black Belt. Table of Contents: 1. Introduction 2. How to use AI in your business 3. Choosing your first AI project 4. Linking business and technology 5. What is an ML pipeline, and how does it affect an AI project? 6. Analyzing an ML pipeline 7. Guiding an AI project to success 8. AI trends that may affect you

Get the eBook free when you register your print book at Manning. This book shows you exactly how to guide large language models from research prototypes to scalable, robust, and efficient production systems. From model training to maintenance, an engineer will find everything they need to work with LLMs in this one-stop guide. This book complements Sebastian Raschka’s Build a Large Language Model (From Scratch), which takes a hands-on, ground-up approach to constructing LLMs. While Raschka’s book focuses on building models from scratch, this book centers on deploying, optimizing, and maintaining reliable, production-grade AI systems. Inside Building Reliable AI Systems you’ll learn how to: • Deploy LLMs into production • Detect and reduce hallucinations • Mitigate bias • Optimize LLM performance and resource usage • Advanced prompt engineering techniques • Build intelligent agents and Retrieval-Augmented Generation Building Reliable AI Systems is a guide to putting LLMs into production in the real world. The book bridges the gap between theory and practice. You’ll go beyond basics like prompting into advanced optimizations: intelligent agents, Retrieval Augmented Generation (RAG), and in-depth solutions for mitigating hallucinations and bias. About the book Building Reliable AI Systems is a comprehensive guide to creating LLM-based apps that are faster and more accurate. It takes you from training to production and beyond into the ongoing maintenance of an LLM. In each chapter, you’ll find in-depth code samples and hands-on projects—including building a RAG-powered chatbot and an agent created with LangChain. Deploying an LLM can be costly, so you’ll love the performance optimization techniques—prompt optimization, model compression, and quantization—that make your LLMs quicker and more efficient. Throughout, real-world case studies from e-commerce, healthcare, and legal work give concrete examples of how businesses have solved some of LLMs common problems. About the reader For data scientists or software engineers confident in Python and NLP. About the author Rush Shahani is a seasoned AI Engineer and CTO of Persana AI, a YCombinator-backed startup. At Persana, he leads the development of natural language processing and large language model systems that provide actionable insights to companies in order to drive revenue growth. His experience includes building AI systems at companies like LinkedIn, Element AI, and Shopify.

Goes beyond academic discussions deeply into the applications layer of Foundation Models. This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice. In LLMs in Production you will: • Grasp the fundamentals of LLMs and the technology behind them • Evaluate when to use a premade LLM and when to build your own • Efficiently scale up an ML platform to handle the needs of LLMs • Train LLM foundation models and finetune an existing LLM • Deploy LLMs to the cloud and edge devices using complex architectures like PEFT and LoRA • Build applications leveraging the strengths of LLMs while mitigating their weaknesses LLMs in Production delivers vital insights into delivering MLOps so you can easily and seamlessly guide one to production usage. Inside, you’ll find practical insights into everything from acquiring an LLM-suitable training dataset, building a platform, and compensating for their immense size. Plus, tips and tricks for prompt engineering, retraining and load testing, handling costs, and ensuring security. Foreword by Joe Reis. About the technology Most business software is developed and improved iteratively, and can change significantly even after deployment. By contrast, because LLMs are expensive to create and difficult to modify, they require meticulous upfront planning, exacting data standards, and carefully-executed technical implementation. Integrating LLMs into production products impacts every aspect of your operations plan, including the application lifecycle, data pipeline, compute cost, security, and more. Get it wrong, and you may have a costly failure on your hands. About the book LLMs in Production teaches you how to develop an LLMOps plan that can take an AI app smoothly from design to delivery. You’ll learn techniques for preparing an LLM dataset, cost-efficient training hacks like LORA and RLHF, and industry benchmarks for model evaluation. Along the way, you’ll put your new skills to use in three exciting example projects: creating and training a custom LLM, building a VSCode AI coding extension, and deploying a small model to a Raspberry Pi. What's inside • Balancing cost and performance • Retraining and load testing • Optimizing models for commodity hardware • Deploying on a Kubernetes cluster About the reader For data scientists and ML engineers who know Python and the basics of cloud deployment. About the author Christopher Brousseau and Matt Sharp are experienced engineers who have led numerous successful large scale LLM deployments.

A hands-on guide for delivering value with AI-driven products! Learn how AI can improve content creation, accelerate data analysis, and upgrade process automation. The Art of AI Product Development offers a clear, practical approach to creating products that use AI. It provides real-world guidance on defining your AI strategy, developing useful AI features, and supporting user trust and adoption. Rather than chasing trends, the book focuses on core principles and long-term thinking—foundations that remain relevant as the field evolves. Inside The Art of AI Product Development, you will learn vital skills for the effective use of AI, including: • Identifying market and business opportunities for AI • Gaining an in-depth understanding of modern AI approaches, incl. predictive AI, LLMs, Retrieval-Augmented Generation, and agent systems • Assembling AI solutions that work, without the hype • Efficiently communicating with data scientists and ML engineers • Designing user-friendly AI interfaces that emphasize trust and transparency • Implementing safe, ethical AI with proper governance processes The Art of AI Product Development is written for product managers, tech executives, UX designers, and anyone responsible for the success of an AI-driven product. It introduces a broad spectrum of AI opportunities and case studies from different domains such as marketing, supply chain, and logistics. You’ll carefully progress from initial design conversations, through to efficient and secure development, and on to deployment and day-to-day management of AI-driven applications. About the technology Integrating AI into your software and processes can create real value for your business and its customers—if you do it right. When you’re on the hook for delivering AI-enabled products, you’ll need to spot high-impact opportunities, work effectively with engineers, design user-centric features, avoid common project failures, and manage real-world launches. This book shows you how. About the book The Art of AI Product Development gives you a clear framework, practical tools, and real-world examples to build confidence and succeed with new AI projects—even if you’re tackling AI for the first time. You’ll love the practical use cases and end-to-end scenarios from domains such as marketing, supply chain management, and sustainability. What's inside • Ideate, shape, and prioritize AI opportunities • Develop AI systems with techniques such as prompt engineering, RAG, and predictive AI • Communicate with different AI stakeholders and promote AI adoption About the reader Written for software product managers, business-oriented engineers, UX designers, startup founders, and anyone responsible for developing, designing, or marketing AI products. No experience with AI required. About the author Dr. Janna Lipenkova is the founder of an AI and analytics business where she has successfully managed AI projects for world-class companies like BMW, Lufthansa, and Volkswagen. Table of Contents Part 1 1 Creating value with AI-driven products 2 Discovering and prioritizing AI opportunities 3 Mapping the AI solution space Part 2 4 Predictive AI 5 Exploring and evaluating language models 6 Prompt engineering 7 Search and retrieval-augmented generation 8 Fine-tuning language models 9 Automating workflows with agentic AI Part 3 10 AI user experience: Designing for uncertainty 11 AI governance 12 Working with your stakeholders Appendix A AI development toolbox

Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book. Modern finance involves crunching more data that any single human can efficiently and effectively process. Sophisticated machine learning models and applications have dominated the high-end fintech industry for decades. Now that extraordinarily powerful AI technologies are readily available to everyone, you can take advantage of deep learning, graph analytics, and large language models (LLMs) to create your own custom finance applications. Financial AI needs to navigate rapidly changing conditions, process incredibly complex data, make split-second decisions and, of course, do it all within the restrictions of regulatory compliance. Author Taehun Kim has spent over a decade building AI systems that perform on the front lines of the finance industry. In Financial AI in Practice you’ll learn how to: • Build end-to-end AI pipelines for credit scoring and fraud detection • Design hybrid strategies combining ML models and LLM-driven insights • Expose complex fraud using graph analytics and network detection • Architect secure, compliant generative AI with RAG techniques • Navigate AI business strategy, ROI, and stakeholder alignment About the book Financial AI in Practice shows you how to deliver financial AI solutions that are more than just a few deployed algorithms. You’ll learn to build a complete, compliant application using the kind of messy, imperfect data you'll encounter in industry. The book introduces around four complete, production-minded systems that handle the core tasks of credit, fraud, investment, and operational efficiency. You’ll build an end-to-end pipeline that assesses credit risk, use supervised, unsupervised, and graph-based models to detect fraud, and combine a quantitative model with LLM-powered news analyses for a hybrid investment strategy. As you build, you’ll master Taehun’s simple-but-powerful 4-Layer Framework, a mental model you can apply to any AI project in finance. About the reader For data scientists, product owners, business leaders, product strategists, and financial analysts. About the author Taehun Kim is a Staff Data Scientist at a major NYSE-listed e-commerce company, where he spearheads fintech initiatives that process millions of transactions daily.

AI doesn't have to be a black box. These practical techniques help shine a light on your model's mysterious inner workings. Make your AI more transparent, and you'll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model.

Everything you need to know about AI to survive—and thrive—as an engineer. If you’re worried about your tech career going obsolete in a world of super-powered AI, never fear. The AI Pocket Book crams everything engineers need to know about AI into one short volume you can fit into your pocket. You’ll build a better understanding of AI (and its limitations), learn how to use it more effectively, and future-proof your job against its advancement. In The AI Pocket Book you’ll find no-nonsense advice on: • Deciphering AI jargon (there’s lots of it!) • Where AI fits within your field of engineering • Why AI hallucinates—and what to do about it • What to do when AI comes for your job • The dark side of AI—copyright, snake oil, and replacing humans • Balancing skepticism with unrealistic expectations The AI Pocket Book gives you Emmanuel Maggiori’s unvarnished and opinionated take on where AI can be useful, and where it still kind of sucks. Whatever your tech field, this short-and-sweet guide delivers the facts and techniques you’ll need in the workplace of the present. About the technology You don’t have to know everything about AI to get a big payoff! Whether you’re looking to boost your coding speed, generate ideas for your next project, or just get a helping hand with your next Medium article, there’s an AI-powered tool ready to assist. This fit-in-your pocket guide tells you everything you need to surf the AI wave instead of drowning in it. About the book The AI Pocket Book takes a peek inside the AI black box and gives you just enough on key topics like transformers, hallucinations, and the modern ecosystem of AI models and tools. You’ll get handy techniques to select AI tools, learn when putting AI first is the smart move, and pick up some excellent tips for managing the inevitable, potentially expensive, screw ups. What's inside • Deciphering AI jargon (there’s lots of it!) • Evaluating AI tools • Why AI hallucinates and what to do about it • How and when to use AI About the reader For engineers in all fields, from software to security. About the author Emmanuel Maggiori, PhD, is a software engineer and 10-year AI industry insider. He is also the author of Smart Until It’s Dumb and Siliconned. Table of Contents 1 How AI works 2 Hallucinations 3 Selecting and evaluating AI tools 4 When to use (and not to use) AI 5 How AI will affect jobs and how to stay ahead 6 The fine print A Catalog of generative AI tools