Programming Large Language Models With Azure Open Ai
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Programming Large Language Models with Azure Open AI
Author: Francesco Esposito
language: en
Publisher: Microsoft Press
Release Date: 2024-04-03
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
Clean Architecture with .NET
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
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.