Download or read book Essential Guide to LLMOps written by RYAN. DOAN and published by Packt Publishing Ltd. This book was released on 2024-07-31 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the secrets to mastering LLMOps with innovative approaches to streamline AI workflows, improve model efficiency, and ensure robust scalability, revolutionizing your language model operations from start to finish Key Features Gain a comprehensive understanding of LLMOps, from data handling to model governance Leverage tools for efficient LLM lifecycle management, from development to maintenance Discover real-world examples of industry cutting-edge trends in generative AI operation Purchase of the print or Kindle book includes a free PDF eBook Book Description The rapid advancements in large language models (LLMs) bring significant challenges in deployment, maintenance, and scalability. This Essential Guide to LLMOps provides practical solutions and strategies to overcome these challenges, ensuring seamless integration and the optimization of LLMs in real-world applications. This book takes you through the historical background, core concepts, and essential tools for data analysis, model development, deployment, maintenance, and governance. You’ll learn how to streamline workflows, enhance efficiency in LLMOps processes, employ LLMOps tools for precise model fine-tuning, and address the critical aspects of model review and governance. You’ll also get to grips with the practices and performance considerations that are necessary for the responsible development and deployment of LLMs. The book equips you with insights into model inference, scalability, and continuous improvement, and shows you how to implement these in real-world applications. By the end of this book, you’ll have learned the nuances of LLMOps, including effective deployment strategies, scalability solutions, and continuous improvement techniques, equipping you to stay ahead in the dynamic world of AI. What you will learn Understand the evolution and impact of LLMs in AI Differentiate between LLMOps and traditional MLOps Utilize LLMOps tools for data analysis, preparation, and fine-tuning Master strategies for model development, deployment, and improvement Implement techniques for model inference, serving, and scalability Integrate human-in-the-loop strategies for refining LLM outputs Grasp the forefront of emerging technologies and practices in LLMOps Who this book is for This book is for machine learning professionals, data scientists, ML engineers, and AI leaders interested in LLMOps. It is particularly valuable for those developing, deploying, and managing LLMs, as well as academics and students looking to deepen their understanding of the latest AI and machine learning trends. Professionals in tech companies and research institutions, as well as anyone with foundational knowledge of machine learning will find this resource invaluable for advancing their skills in LLMOps.

Download or read book Machine Learning Upgrade written by Kristen Kehrer and published by John Wiley & Sons. This book was released on 2024-07-29 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: A much-needed guide to implementing new technology in workspaces From experts in the field comes Machine Learning Upgrade: A Data Scientist's Guide to MLOps, LLMs, and ML Infrastructure, a book that provides data scientists and managers with best practices at the intersection of management, large language models (LLMs), machine learning, and data science. This groundbreaking book will change the way that you view the pipeline of data science. The authors provide an introduction to modern machine learning, showing you how it can be viewed as a holistic, end-to-end system—not just shiny new gadget in an otherwise unchanged operational structure. By adopting a data-centric view of the world, you can begin to see unstructured data and LLMs as the foundation upon which you can build countless applications and business solutions. This book explores a whole world of decision making that hasn't been codified yet, enabling you to forge the future using emerging best practices. Gain an understanding of the intersection between large language models and unstructured data Follow the process of building an LLM-powered application while leveraging MLOps techniques such as data versioning and experiment tracking Discover best practices for training, fine tuning, and evaluating LLMs Integrate LLM applications within larger systems, monitor their performance, and retrain them on new data This book is indispensable for data professionals and business leaders looking to understand LLMs and the entire data science pipeline.

Download or read book Mastering Large Language Models with Python written by Raj Arun R and published by Orange Education Pvt Ltd. This book was released on 2024-04-12 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index

Download or read book Generative AI Security written by Ken Huang and published by Springer Nature. This book was released on with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mastering Large Language Models written by Sanket Subhash Khandare and published by BPB Publications. This book was released on 2024-03-12 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do not just talk AI, build it: Your guide to LLM application development KEY FEATURES ● Explore NLP basics and LLM fundamentals, including essentials, challenges, and model types. ● Learn data handling and pre-processing techniques for efficient data management. ● Understand neural networks overview, including NN basics, RNNs, CNNs, and transformers. ● Strategies and examples for harnessing LLMs. DESCRIPTION Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks , and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices. WHAT YOU WILL LEARN ● Grasp fundamentals of natural language processing (NLP) applications. ● Explore advanced architectures like transformers and their applications. ● Master techniques for training large language models effectively. ● Implement advanced strategies, such as meta-learning and self-supervised learning. ● Learn practical steps to build custom language model applications. WHO THIS BOOK IS FOR This book is tailored for those aiming to master large language models, including seasoned researchers, data scientists, developers, and practitioners in natural language processing (NLP). TABLE OF CONTENTS 1. Fundamentals of Natural Language Processing 2. Introduction to Language Models 3. Data Collection and Pre-processing for Language Modeling 4. Neural Networks in Language Modeling 5. Neural Network Architectures for Language Modeling 6. Transformer-based Models for Language Modeling 7. Training Large Language Models 8. Advanced Techniques for Language Modeling 9. Top Large Language Models 10. Building First LLM App 11. Applications of LLMs 12. Ethical Considerations 13. Prompt Engineering 14. Future of LLMs and Its Impact

Download or read book Adversarial AI Attacks Mitigations and Defense Strategies written by John Sotiropoulos and published by Packt Publishing Ltd. This book was released on 2024-07-26 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST Key Features Understand the connection between AI and security by learning about adversarial AI attacks Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAdversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you’ll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI systems effectively.What you will learn Understand poisoning, evasion, and privacy attacks and how to mitigate them Discover how GANs can be used for attacks and deepfakes Explore how LLMs change security, prompt injections, and data exposure Master techniques to poison LLMs with RAG, embeddings, and fine-tuning Explore supply-chain threats and the challenges of open-access LLMs Implement MLSecOps with CIs, MLOps, and SBOMs Who this book is for This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you’ll need a basic understanding of security, ML concepts, and Python.

Download or read book Developing ASP Components written by Shelley Powers and published by O'Reilly Media. This book was released on 1999 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: The popularity of Microsoft's Active Server Pages (ASP) technology is growing rapidly. Part of the reason is ASP's flexibility: the output of ASP scripts is most commonly HTML, which is included in the text stream returned to the client, making it a convenient way of creating browser-independent web content. But an additional reason--and one that will become more and more important over time, as webapplications replace web pages--is its extensibility. And the most effective way to extend ASP is to develop custom ASP components. However, the techniques for developing custom ASP components, along with the snags and pitfalls of developing custom components, are not well documented. In addition, to successfully develop ASP components one must be a jack-of-all-trades: programming requires some knowledge of COM, of threading models, and of the ASP object model, as well as a mastery of one or more language tools and development environments. That's where Developing ASP Components comes in. The first section of the book explores the topics all developers need to know to develop components for ASP effectively: The configuration of the ASP development environment ASP components and the Component Object Model (COM) ASP components and threading models ASP components and the Microsoft Transaction Server, which can be used to provide a variety of services to ASP components The objects, properties, methods, and events available in the ASP object model Because more and more developers find themselves using more than a single language tool, the remaining three sections of the book each focus on ASP component development using any of the three major development tools: Microsoft Visual Basic, Microsoft Visual C++ and the ActiveX Template Library (ATL), and Microsoft J++. Each section carefully focuses on the issues that concern the ASP component developer who is using that particular development environment. These include: Accessing ASP's intrinsic objects Accessing data using either OLE DB (in the case of C++) or ADO (in the case of VB and J++) Creating n-tier web applications with VB Handling persistence using MFC along with Visual C++/ATL Accessing native code (the Windows libraries, which are written in C) from J++ This thorough coverage of the background information needed for developing ASP components, as well as its focus on the component development in each of three major development environments, makes Developing ASP Components the definitive resource for the ASP application and component developer.

Download or read book Penetration Tester s Open Source Toolkit written by Jeremy Faircloth and published by Elsevier. This book was released on 2011-08-25 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Penetration Tester’s Open Source Toolkit, Third Edition, discusses the open source tools available to penetration testers, the ways to use them, and the situations in which they apply. Great commercial penetration testing tools can be very expensive and sometimes hard to use or of questionable accuracy. This book helps solve both of these problems. The open source, no-cost penetration testing tools presented do a great job and can be modified by the student for each situation. This edition offers instruction on how and in which situations the penetration tester can best use them. Real-life scenarios support and expand upon explanations throughout. It also presents core technologies for each type of testing and the best tools for the job. The book consists of 10 chapters that covers a wide range of topics such as reconnaissance; scanning and enumeration; client-side attacks and human weaknesses; hacking database services; Web server and Web application testing; enterprise application testing; wireless penetrating testing; and building penetration test labs. The chapters also include case studies where the tools that are discussed are applied. New to this edition: enterprise application testing, client-side attacks and updates on Metasploit and Backtrack. This book is for people who are interested in penetration testing or professionals engaged in penetration testing. Those working in the areas of database, network, system, or application administration, as well as architects, can gain insights into how penetration testers perform testing in their specific areas of expertise and learn what to expect from a penetration test. This book can also serve as a reference for security or audit professionals. Details current open source penetration testing tools Presents core technologies for each type of testing and the best tools for the job New to this edition: Enterprise application testing, client-side attacks and updates on Metasploit and Backtrack

Download or read book Agile and Iterative Development written by Craig Larman and published by Addison-Wesley Professional. This book was released on 2004 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the definitive guide for managers and students to agile and iterativedevelopment methods: what they are, how they work, how to implement them, andwhy they should.

Download or read book PCR Troubleshooting and Optimization written by Suzanne Kennedy and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the strategies for preparing effective controls and standards for PCR, when they should be employed and how to interpret the information they provide. It highlights the significance of optimization for efficiency, precision and sensitivity of PCR methodology and provides essential guidance on how to troubleshoot inefficient reactions. Experts in PCR describe design and optimization techniques, discuss the use of appropriate controls, explain the significance of standard curves and explore the principles and strategies required for effective troubleshooting. Authors highligh.

Download or read book Machine Learning Engineering in Action written by Ben Wilson and published by Simon and Schuster. This book was released on 2022-05-17 with total page 879 pages. Available in PDF, EPUB and Kindle. Book excerpt: Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer.

Download or read book Chordomas written by M. Necmettin Pamir and published by Thieme Medical Publishers. This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The definitive guide on chordomas by an international Who's Who of experts A rare form of cancer, chordomas are among the most difficult tumors to treat, requiring highly specialized training and surgical expertise. Although generally slow-growing, chordomas present resection challenges due to their proximity to critical structures including the spinal cord, brainstem, nerves, and arteries. Written by an impressive cadre of internationally-renowned experts, this textbook is the most current, concise, and definitive resource on diagnosis and management of this complex pathology. Acclaimed surgeons from Brazil, Canada, Japan, Turkey, the USA, and the UK are contributors. The book starts with a historical overview, epidemiology, molecular pathogenesis, cytogenetics, local invasion and metastasis, and pathology. A thorough discussion covers diagnostic radiological and radionuclide imaging such as MRI, PET; SPECT, PET-CT, and PET-MRI, and clinical traits of skull base and spinal chordomas as defined by imaging modalities. Subsequent chapters explore decision making including selecting the most optimal techniques, surgical procedures, specialized topics, and new horizons in chordoma treatment. Key Highlights Anterior and anterolateral approaches to the clivus The use of endoscopy and intraoperative assistive technologies Midline subfrontal, middle fossa, extended petrosal, and transcondylar approaches Craniospinal fusion techniques following resection Surgery for spinal chordomas from the cervical to the sacral region Special topics such as radiation, proton beam therapy, Gamma-Knife radiosurgery, and pediatric chordomas and chondrosarcomas This is an essential text for neurosurgeons, orthopedic spine surgeons, otolaryngologists, and radiation oncologists. Read and learn from innovators who have mastered the latest state-of-the-art treatment methodologies for chordomas.

Download or read book Introducing MLOps written by Mark Treveil and published by "O'Reilly Media, Inc.". This book was released on 2020-11-30 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized

Download or read book Technical Analysis And Financial Asset Forecasting From Simple Tools To Advanced Techniques written by Raymond Hon-fu Chan and published by World Scientific Publishing Company. This book was released on 2014-08-19 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technical analysis is defined as the tracking and prediction of asset price movements using charts and graphs in combination with various mathematical and statistical methods. More precisely, it is the quantitative criteria used in predicting the relative strength of buying and selling forces within a market to determine what to buy, what to sell, and when to execute trades. This book introduces simple technical analysis tools like moving averages and Bollinger bands, and also advanced techniques such as wavelets and empirical mode decomposition. It first discusses some traditional tools in technical analysis, such as trend, trend Line, trend channel, Gann's Theory, moving averages, and Bollinger bands. It then introduces a recent indicator developed for stock market and two recent techniques used in the technical analysis field: wavelets and the empirical mode decomposition in financial time series. The book also discusses the theory to test the performance of the indicators and introduces the MATLAB Financial Toolbox, some of the functions/codes of which are used in our numerical experiments.

Download or read book Automated Evaluation of Text and Discourse with Coh Metrix written by Danielle S. McNamara and published by Cambridge University Press. This book was released on 2014-03-24 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Coh-Metrix is among the broadest and most sophisticated automated textual assessment tools available today. Automated Evaluation of Text and Discourse with Coh-Metrix describes this computational tool, as well as the wide range of language and discourse measures it provides. Part I of the book focuses on the theoretical perspectives that led to the development of Coh-Metrix, its measures, and empirical work that has been conducted using this approach. Part II shifts to the practical arena, describing how to use Coh-Metrix and how to analyze, interpret, and describe results. Coh-Metrix opens the door to a new paradigm of research that coordinates studies of language, corpus analysis, computational linguistics, education, and cognitive science. This tool empowers anyone with an interest in text to pursue a wide array of previously unanswerable research questions.

Download or read book Large Language Models written by Uday Kamath and published by Springer Nature. This book was released on 2024 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.

Download or read book Machine Learning Design Patterns written by Valliappa Lakshmanan and published by O'Reilly Media. This book was released on 2020-10-15 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly