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Programming Large Language Models with Azure Open AI

Programming Large Language Models with Azure Open AI
Author: Francesco Esposito
Publisher: Microsoft Press
Total Pages: 605
Release: 2024-04-03
Genre: Computers
ISBN: 0138280452
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Use LLMs to build better business software applications Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural. Artificial Intelligence expert Francesco Esposito illustrates several scenarios for which a LLM is effective: crafting sophisticated business solutions, shortening the gap between humans and software-equipped machines, and building powerful reasoning engines. Insight into prompting and conversational programming—with specific techniques for patterns and frameworks—unlock how natural language can also lead to a new, advanced approach to coding. Concrete end-to-end demonstrations (featuring Python and ASP.NET Core) showcase versatile patterns of interaction between existing processes, APIs, data, and human input. Artificial Intelligence expert Francesco Esposito helps you: Understand the history of large language models and conversational programming Apply prompting as a new way of coding Learn core prompting techniques and fundamental use-cases Engineer advanced prompts, including connecting LLMs to data and function calling to build reasoning engines Use natural language in code to define workflows and orchestrate existing APIs Master external LLM frameworks Evaluate responsible AI security, privacy, and accuracy concerns Explore the AI regulatory landscape Build and implement a personal assistant Apply a retrieval augmented generation (RAG) pattern to formulate responses based on a knowledge base Construct a conversational user interface For IT Professionals and Consultants For software professionals, architects, lead developers, programmers, and Machine Learning enthusiasts For anyone else interested in natural language processing or real-world applications of human-like language in software

Azure OpenAI Service for Cloud Native Applications

Azure OpenAI Service for Cloud Native Applications
Author: Adrián González Sánchez
Publisher: "O'Reilly Media, Inc."
Total Pages: 249
Release: 2024-06-27
Genre: Computers
ISBN: 1098154967
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Get the details, examples, and best practices you need to build generative AI applications, services, and solutions using the power of Azure OpenAI Service. With this comprehensive guide, Microsoft AI specialist Adrián González Sánchez examines the integration and utilization of Azure OpenAI Service—using powerful generative AI models such as GPT-4 and GPT-4o—within the Microsoft Azure cloud computing platform. To guide you through the technical details of using Azure OpenAI Service, this book shows you how to set up the necessary Azure resources, prepare end-to-end architectures, work with APIs, manage costs and usage, handle data privacy and security, and optimize performance. You'll learn various use cases where Azure OpenAI Service models can be applied, and get valuable insights from some of the most relevant AI and cloud experts. Ideal for software and cloud developers, product managers, architects, and engineers, as well as cloud-enabled data scientists, this book will help you: Learn how to implement cloud native applications with Azure OpenAI Service Deploy, customize, and integrate Azure OpenAI Service with your applications Customize large language models and orchestrate knowledge with company-owned data Use advanced roadmaps to plan your generative AI project Estimate cost and plan generative AI implementations for adopter companies

Azure OpenAI Service for Cloud Native Applications

Azure OpenAI Service for Cloud Native Applications
Author: Adrián González Sánchez
Publisher: "O'Reilly Media, Inc."
Total Pages: 275
Release: 2024-06-27
Genre: Computers
ISBN: 1098154959
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Get the details, examples, and best practices you need to build generative AI applications, services, and solutions using the power of Azure OpenAI Service. With this comprehensive guide, Microsoft AI specialist Adrián González Sánchez examines the integration and utilization of Azure OpenAI Service—using powerful generative AI models such as GPT-4 and GPT-4o—within the Microsoft Azure cloud computing platform. To guide you through the technical details of using Azure OpenAI Service, this book shows you how to set up the necessary Azure resources, prepare end-to-end architectures, work with APIs, manage costs and usage, handle data privacy and security, and optimize performance. You'll learn various use cases where Azure OpenAI Service models can be applied, and get valuable insights from some of the most relevant AI and cloud experts. Ideal for software and cloud developers, product managers, architects, and engineers, as well as cloud-enabled data scientists, this book will help you: Learn how to implement cloud native applications with Azure OpenAI Service Deploy, customize, and integrate Azure OpenAI Service with your applications Customize large language models and orchestrate knowledge with company-owned data Use advanced roadmaps to plan your generative AI project Estimate cost and plan generative AI implementations for adopter companies

Clean Architecture with .NET

Clean Architecture with .NET
Author: Dino Esposito
Publisher: Microsoft Press
Total Pages: 679
Release: 2024-03-12
Genre: Computers
ISBN: 0138203423
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Understand what to do at any point in developing a clean .NET architecture Master advanced .NET techniques with a focus on actual value delivered by working within a modular, clean architecture. Microsoft Data Platform MVP Dino Esposito explains key clean architecture concepts with a mix of pragmatism and design discipline and helps you solidify your knowledge through a real-world project. Starting with an explanation of the quest for modular software architecture continuing through the methodology of domain-driven design (DDD), Esposito emphasizes the role that modularization plays in managing complexity in software development. Breaking down the layers of an architecture that is modular and maintainable, he presents a sample project that is not simply another to-do list, but an actual tool for the reader. Ultimately, an exploration of common dilemmas for both developers and operations brings together historical developments with real solutions for today. Microsoft Data Platform MVP Dino Esposito helps you: · Understand the relevance of modular software architecture in the history of software · Review domain-driven design concepts both, strategic and practical · Apply modular analysis techniques to your development · Make the most of layered architecture · Make the most of layered architecture that is modular and maintainable · Explore in detail the individual layers—presentation, application, domain and infrastructure · Make sense of domain services to separate raw persistence from persistence-related business tasks · Make your way through a series of C# best-practices for modeling classes from real-world entities · Understand the benefits of microservices versus modular monoliths · Understand the analysis of technical shortcuts and benefits of long-term technical investment · Understand client-side, server-side and other common deployment dilemmas · Set up your architecture, test your conclusions, and find even more help

Responsible AI in the Enterprise

Responsible AI in the Enterprise
Author: Adnan Masood
Publisher: Packt Publishing Ltd
Total Pages: 318
Release: 2023-07-31
Genre: Computers
ISBN: 1803249668
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Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfalls Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn ethical AI principles, frameworks, and governance Understand the concepts of fairness assessment and bias mitigation Introduce explainable AI and transparency in your machine learning models Book DescriptionResponsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations. By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.What you will learn Understand explainable AI fundamentals, underlying methods, and techniques Explore model governance, including building explainable, auditable, and interpretable machine learning models Use partial dependence plot, global feature summary, individual condition expectation, and feature interaction Build explainable models with global and local feature summary, and influence functions in practice Design and build explainable machine learning pipelines with transparency Discover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platforms Who this book is for This book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.

C# Interview Guide

C# Interview Guide
Author: Konstantin Semenenko
Publisher: Packt Publishing Ltd
Total Pages: 362
Release: 2024-03-08
Genre: Computers
ISBN: 1805123580
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Catapult your C# journey with this guide to crafting standout resumes, mastering advanced concepts, and navigating job offers with real-world insights for unparalleled success in programming and interviews Key Features Acquire a strong foundation in syntax, data types, and object-oriented programming to code confidently Develop strategies for addressing behavioral questions, tackle technical challenges, and showcase your coding skills Augment your C# programming skills with valuable insights from industry experts Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIf you're gearing up for technical interviews by enhancing your programming skills and aiming for a successful career in C# programming and software development, the C# Interview Guide is your key to interview success. Designed to equip you with essential skills for excelling in technical interviews, this guide spans a broad spectrum, covering fundamental C# programming concepts to intricate technical details. As you progress, you'll develop proficiency in crafting compelling resumes, adeptly answering behavioral questions, and navigating the complexities of salary negotiations and job evaluations. What sets this book apart is its coverage, extending beyond technical know-how and incorporating real-world experiences and expert insights from industry professionals. This comprehensive approach, coupled with guidance on overcoming challenges, ranging from interview preparation to post-interview strategies, makes this guide an invaluable resource for those aspiring to advance in their C# programming careers. By the end of this guide, you’ll emerge with a solid understanding of C# programming, advanced technical interview skills, and the ability to apply industry best practices.What you will learn Craft compelling resumes and cover letters for impactful job applications Demonstrate proficiency in fundamental C# programming concepts and syntax Master advanced C# topics, including LINQ, asynchronous programming, and design patterns Implement best practices for writing clean, maintainable C# code Use popular C# development tools and frameworks, such as .NET and .NET Core Negotiate salary, evaluate job offers, and build a strong C# portfolio Apply soft skills for successful interactions in C# development roles Who this book is for This book is for individuals aspiring to pursue a career in C# programming or software development. Whether you are a beginner or experienced professional, this guide will enhance your technical interview skills and C# programming knowledge.

Azure AI Services at Scale for Cloud, Mobile, and Edge

Azure AI Services at Scale for Cloud, Mobile, and Edge
Author: Simon Bisson
Publisher: "O'Reilly Media, Inc."
Total Pages: 234
Release: 2022-04-11
Genre: Computers
ISBN: 1098107993
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Take advantage of the power of cloud and the latest AI techniques. Whether you're an experienced developer wanting to improve your app with AI-powered features or you want to make a business process smarter by getting AI to do some of the work, this book's got you covered. Authors Anand Raman, Chris Hoder, Simon Bisson, and Mary Branscombe show you how to build practical intelligent applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. This book shows you how cloud AI services fit in alongside familiar software development approaches, walks you through key Microsoft AI services, and provides real-world examples of AI-oriented architectures that integrate different Azure AI services. All you need to get started is a working knowledge of basic cloud concepts. Become familiar with Azure AI offerings and capabilities Build intelligent applications using Azure Cognitive Services Train, tune, and deploy models with Azure Machine Learning, PyTorch, and the Open Neural Network Exchange (ONNX) Learn to solve business problems using AI in the Power Platform Use transfer learning to train vision, speech, and language models in minutes

GPT-3

GPT-3
Author: Sandra Kublik
Publisher: Packt Publishing Ltd
Total Pages: 151
Release: 2023-02-13
Genre: Computers
ISBN: 1805120883
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GPT-3: The Ultimate Guide To Building NLP Products With OpenAI API is a comprehensive book on the Generative Pre-trained Transformer 3 AI language model, covering its significance, capabilities, and application in creating innovative NLP Products. Key FeaturesExploration of GPT-3: The book explores GPT-3, a powerful language model, and its capabilitiesBusiness applications: The book provides practical knowledge on using GPT-3 to create new business productsExamination of AI trends: The book examines the impact of GPT-3 on emerging creator economy and trends like no-code & AGIBook Description GPT-3 has made creating AI apps simpler than ever. This book provides a comprehensive guide on how to utilize the OpenAI API with ease. It explores imaginative methods of utilizing this tool for your specific needs and showcases successful businesses that have been established through its use. The book is divided into two sections, with the first focusing on the fundamentals of the OpenAI API. The second part examines the dynamic and thriving environment that has arisen around GPT-3. Chapter 1 sets the stage with background information and defining key terms. Chapter 2 goes in-depth into the API, breaking it down into its essential components, explaining their functions and offering best practices. Chapter 3, you will build your first app with GPT-3. Chapter 4 features interviews with the founders of successful GPT-3-based products, who share challenges and insights gained. Chapter 5 examines the perspective of enterprises on GPT-3 and its potential for adoption. The problematic consequences of widespread GPT-3 adoption, such as misapplication and bias, are addressed along with efforts to resolve these issues in Chapter 6. Finally, Chapter 7 delves into the future by exploring the most exciting trends and possibilities as GPT-3 becomes increasingly integrated into the commercial ecosystem. What you will learnLearn the essential components of the OpenAI API along with the best practicesBuild and deploy your first GPT-3 powered applicationLearn from the journeys of industry leaders, startup founders who have built and deployed GPT-3 based products at scaleLook at how enterprises view GPT-3 and its potential for adoption for scalable solutionsNavigating the Consequences of GPT-3 adoption and efforts to resolve themExplore the exciting trends and possibilities of combining models with GPT-3 with No codeWho this book is for This book caters to individuals from diverse backgrounds, not just technical experts. It should be useful to you if you are:A data expert seeking to improve your AI expertiseAn entrepreneur looking to revolutionize the AI industryA business leader seeking to enhance your AI knowledge and apply it to informed decision makingA content creator in the language domain looking to utilize GPT-3's language abilities for creative and imaginative projectsAnyone with an AI idea that was previously deemed technically unfeasible or too costly to execute

Programming ML.NET

Programming ML.NET
Author: Dino Esposito
Publisher: Microsoft Press
Total Pages: 549
Release: 2022-02-03
Genre: Computers
ISBN: 0137383622
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The expert guide to creating production machine learning solutions with ML.NET! ML.NET brings the power of machine learning to all .NET developers— and Programming ML.NET helps you apply it in real production solutions. Modeled on Dino Esposito's best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsoft's team used to build ML.NET itself. After a foundational overview of ML.NET's libraries, the authors illuminate mini-frameworks (“ML Tasks”) for regression, classification, ranking, anomaly detection, and more. For each ML Task, they offer insights for overcoming common real-world challenges. Finally, going far beyond shallow learning, the authors thoroughly introduce ML.NET neural networking. They present a complete example application demonstrating advanced Microsoft Azure cognitive services and a handmade custom Keras network— showing how to leverage popular Python tools within .NET. 14-time Microsoft MVP Dino Esposito and son Francesco Esposito show how to: Build smarter machine learning solutions that are closer to your user's needs See how ML.NET instantiates the classic ML pipeline, and simplifies common scenarios such as sentiment analysis, fraud detection, and price prediction Implement data processing and training, and “productionize” machine learning–based software solutions Move from basic prediction to more complex tasks, including categorization, anomaly detection, recommendations, and image classification Perform both binary and multiclass classification Use clustering and unsupervised learning to organize data into homogeneous groups Spot outliers to detect suspicious behavior, fraud, failing equipment, or other issues Make the most of ML.NET's powerful, flexible forecasting capabilities Implement the related functions of ranking, recommendation, and collaborative filtering Quickly build image classification solutions with ML.NET transfer learning Move to deep learning when standard algorithms and shallow learning aren't enough “Buy” neural networking via the Azure Cognitive Services API, or explore building your own with Keras and TensorFlow

Data Labeling in Machine Learning with Python

Data Labeling in Machine Learning with Python
Author: Vijaya Kumar Suda
Publisher: Packt Publishing Ltd
Total Pages: 398
Release: 2024-01-31
Genre: Computers
ISBN: 1804613789
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Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labeling Key Features Generate labels for regression in scenarios with limited training data Apply generative AI and large language models (LLMs) to explore and label text data Leverage Python libraries for image, video, and audio data analysis and data labeling Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you’ll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learn Excel in exploratory data analysis (EDA) for tabular, text, audio, video, and image data Understand how to use Python libraries to apply rules to label raw data Discover data augmentation techniques for adding classification labels Leverage K-means clustering to classify unsupervised data Explore how hybrid supervised learning is applied to add labels for classification Master text data classification with generative AI Detect objects and classify images with OpenCV and YOLO Uncover a range of techniques and resources for data annotation Who this book is for This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.