Interfacing with Machine Learning by O'Reilly

Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models. Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. You'll learn: Why LLMs present unique security challenges How to navigate the many risk conditions associated with using LLM technology The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained How to identify the top risks and vulnerabilities associated with LLMs Methods for deploying defenses to protect against attacks on top vulnerabilities Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization

Eager to learn AI and machine learning but unsure where to start? Laurence Moroney's hands-on, code-first guide demystifies complex AI concepts without relying on advanced mathematics. Designed for programmers, it focuses on practical applications using PyTorch, helping you build real-world models without feeling overwhelmed. From computer vision and natural language processing (NLP) to generative AI with Hugging Face Transformers, this book equips you with the skills most in demand for AI development today. You'll also learn how to deploy your models across the web and cloud confidently. Gain the confidence to apply AI without needing advanced math or theory expertise Discover how to build AI models for computer vision, NLP, and sequence modeling with PyTorch Learn generative AI techniques with Hugging Face Diffusers and Transformers

Many engineers assume their organization doesn't have an engineering strategy—when in fact, they often do. It just may not be working. In Crafting Engineering Strategy, Will Larson (author of An Elegant Puzzle, Staff Engineer, and The Engineering Executive's Primer) offers a practical, example-rich guide to navigating technical and organizational complexity through structured, intentional strategy. Written for senior engineers, engineering leaders, architects, and curious collaborators, this book lays out a repeatable process for building effective, actionable strategies—from early diagnosis to rollout. With lessons drawn from real-world case studies at companies like Stripe, Uber, and Calm, Larson provides a framework for shaping critical decisions around system migrations, API deprecations, platform investments, and more. Along the way, you'll learn to augment technical planning with communication, governance, and systems thinking. Whether you're shaping your team's direction or leading a company-wide initiative, Crafting Engineering Strategy will help you make thoughtful decisions that stick. Build durable engineering strategies from first principles Apply methods like Wardley mapping and systems modeling Lead strategy as a staff+ engineer or executive Learn from detailed case studies across industries Improve your strategic fluency and influence over time

Generative AI enables powerful new capabilities, but they come with some serious limitations that you'll have to tackle to ship a reliable application or agent. Luckily, experts in the field have compiled a library of 32 tried-and-true design patterns to address the challenges you're likely to encounter when building applications using LLMs, such as hallucinations, nondeterministic responses, and knowledge cutoffs. This book codifies research and real-world experience into advice you can incorporate into your projects. Each pattern describes a problem, shows a proven way to solve it with a fully coded example, and discusses trade-offs. Design around the limitations of LLMs Ensure that generated content follows a specific style, tone, or format Maximize creativity while balancing different types of risk Build agents that plan, self-correct, take action, and collaborate with other agents Compose patterns into agentic applications for a variety of use cases

AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today. You'll learn how to use the power of pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large numbers of text documents; and use existing libraries and pretrained models for text classification, search, and clusterings. This book also shows you how to: Build advanced LLM pipelines to cluster text documents and explore the topics they belong to Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers Learn various use cases where these models can provide value Understand the architecture of underlying Transformer models like BERT and GPT Get a deeper understanding of how LLMs are trained Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning

The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place. With an approachable yet deeply informative style, author Aurélien Géron delivers the ultimate introductory guide to machine learning and deep learning. Drawing on the Hugging Face ecosystem, with a focus on clear explanations and real-world examples, the book takes you through cutting-edge tools like Scikit-Learn and PyTorch—from basic regression techniques to advanced neural networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to build intelligent systems. Understand ML basics, including concepts like overfitting and hyperparameter tuning Complete an end-to-end ML project using scikit-Learn, covering everything from data exploration to model evaluation Learn techniques for unsupervised learning, such as clustering and anomaly detection Build advanced architectures like transformers and diffusion models with PyTorch Harness the power of pretrained models—including LLMs—and learn to fine-tune them Train autonomous agents using reinforcement learning

If you're looking to build a production-ready AI application that enables users to "chat" with your company's private data, then you'll need to master LangChain--a premier AI development framework used by global corporations and startups like Zapier, Replit, Databricks, and more. This guide is an indispensable resource for developers who understand Python or JavaScript but are beginners eager to harness the power of AI. Authors Mayo Oshin and Nuno Campos demystify the use of LangChain through practical insights and in-depth tutorials. Starting with basic concepts, this book will show you step-by-step how to build a production-ready AI chatbot trained on your own data. After reading this book, you'll be equipped to: Understand and use the core components of LangChain in your development projects Harness the power of retrieval-augmented generation (RAG) to enhance the accuracy of LLMs using external, up-to-date data Develop and deploy AI chatbots that interact intelligently and contextually with users Utilize LangChain Expression Language to create custom, efficient AI operational chains Integrate and manage third-party APIs and tools to extend the functionality of your AI applications Learn the foundations of LLM app development and how they can be used with LangChain

Unlock the power of Large Language Models (LLMs) with LLMOps: Foundations, Deployment, and Responsible Operations of Large Language Models, a comprehensive guide tailored for college students and practitioners diving into the dynamic world of AI. This book is your roadmap to mastering the end-to-end process of building, deploying, and managing LLMs in real-world applications, blending cutting-edge theory with practical, hands-on insights. Why This Book? Foundational Knowledge: Start with a solid grounding in AI and machine learning fundamentals, from the evolution of neural networks to the transformer architecture powering modern LLMs. Perfect for students new to the field or professionals looking to deepen their understanding. Practical LLMOps: Learn the art and science of LLMOps, covering data curation, model pretraining, fine-tuning, prompt engineering, and deployment strategies. Master tools like Docker, Kubernetes, and vector databases to build scalable, efficient AI systems. Real-World Applications: Explore industry case studies in healthcare, finance, education, and more, showing how LLMs drive innovation in chatbots, code generation, and personalized learning. Responsible AI: Navigate the ethical and regulatory landscape with guidance on mitigating bias, ensuring privacy, and implementing guardrails for safe, fair AI operations. Future-Ready Insights: Stay ahead with chapters on emerging trends like federated learning, multimodal AI, and sustainable scaling, preparing you for the next decade of AI advancements. Who Should Read This? College Students: Ideal for computer science, data science, or engineering students seeking a clear, structured introduction to LLMs and their operational challenges. Practitioners: Perfect for data scientists, machine learning engineers, and DevOps professionals looking to deploy LLMs in production, optimize performance, and address ethical concerns. Educators: A valuable resource for designing AI and machine learning curricula, with practical examples and appendices on tools, datasets, and CI/CD pipelines. What Sets It Apart? With a clear, engaging style, this book bridges theory and practice, offering actionable checklists, industry case studies, and a glossary of terms to support learning and application. Whether you're building your first AI pipeline or scaling enterprise-grade LLM systems, LLMOps equips you with the knowledge and tools to succeed responsibly in the AI revolution.

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture--and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code

Generative AI has the potential to innovate and evolve business processes, but workers are still figuring out how to build with, optimize, and prompt GenAI tools to fit their needs. And of course, there are pitfalls to avoid, like security risks and hallucinations. Getting it right requires an intuitive understanding of the technology’s capabilities and limitations. This approachable guidebook helps learners of all levels navigate GenAI—and have fun while doing it. Loaded with insightful diagrams and illustrations, Visualizing Generative AI is the perfect entry point for curious IT professionals, business leaders who want to stay on top of the latest technologies, students exploring careers in cloud computing and AI, and anyone else interested in getting started with GenAI. You’ll traverse the generative AI landscape, exploring everything from how this technology works to the ways organizations are already leveraging it to great success. Understand how generative AI has evolved, with a focus on major breakthroughs Get acquainted with the available tools and platforms for GenAI workloads Examine real-world applications, such as chatbots and workflow automation Learn fundamentals that you can build upon as you continue your GenAI journey