Francesco Esposito – författare

Prepare for Microsoft Exam AI-900 and demonstrate your real-world knowledge of diverse machine learning (ML) and artificial intelligence (AI) concepts, and how they can be implemented with Microsoft Azure services. Designed for business stakeholders, new and existing IT professionals, consultants, and students, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure AI Fundamentals level.

Focus on the expertise measured by these objectives:

Describe AI workloads and considerations Describe fundamental principles of machine learning on Azure Describe features of computer vision workloads on Azure Describe features of Natural Language Processing (NLP) workloads on Azure Describe features of conversational AI workloads on Azure

This Microsoft Exam Ref:

Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you are a business user, stakeholder, technical professional, or student who wants to become familiar with Azure AI; requires no data science or software engineering experience.

Master machine learning concepts and develop real-world solutions

Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft’s powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning.

· 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you

· Explore what’s known about how humans learn and how intelligent software is built

· Discover which problems machine learning can address

· Understand the machine learning pipeline: the steps leading to a deliverable model

· Use AutoML to automatically select the best pipeline for any problem and dataset

· Master ML.NET, implement its pipeline, and apply its tasks and algorithms

· Explore the mathematical foundations of machine learning

· Make predictions, improve decision-making, and apply probabilistic methods

· Group data via classification and clustering

· Learn the fundamentals of deep learning, including neural network design

· Leverage AI cloud services to build better real-world solutions faster

About This Book

· For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills

· Includes examples of machine learning coding scenarios built using the ML.NET library

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

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

Programación de modelos de lenguaje de gran tamaño con Azure OpenAI es un eText práctico y actualizado que explora cómo los modelos de lenguaje de gran tamaño (LLM) están redefiniendo el desarrollo de software moderno y la forma en que las personas interactúan con la inteligencia artificial. Basado en tecnologías como Azure OpenAI y las capacidades que impulsan herramientas similares a ChatGPT, este eText guía a los lectores a través de los conceptos fundamentales de los LLM, la ingeniería de prompts y las mejores prácticas para diseñar aplicaciones inteligentes capaces de comprender, generar y utilizar lenguaje natural de forma eficaz.

A medida que avanza el contenido, los lectores descubrirán cómo crear soluciones empresariales impulsadas por IA, desde asistentes personales y experiencias conversacionales hasta aplicaciones que integran datos propios mediante técnicas de Generación Aumentada por Recuperación (RAG). Con ejemplos prácticos, marcos modernos como LangChain y Semantic Kernel, y casos de uso basados en Microsoft Azure, este eText ofrece una combinación equilibrada de fundamentos teóricos y aplicación práctica para profesionales que buscan aprovechar el potencial de la IA generativa en entornos reales.

Introducción a los fundamentos de los modelos de lenguaje de gran tamaño (LLM). Comprensión de la evolución y el impacto de la IA generativa en el desarrollo de software. Técnicas básicas y avanzadas de ingeniería de prompts. Aplicación de patrones de programación conversacional para escenarios empresariales. Uso de marcos modernos como LangChain y Microsoft Semantic Kernel. Implementación de soluciones basadas en Azure OpenAI y servicios de Microsoft Azure. Estrategias para integrar LLM con datos empresariales mediante RAG. Buenas prácticas para abordar seguridad, privacidad, precisión y uso responsable de la IA. Desarrollo de asistentes virtuales e interfaces de usuario conversacionales. Ejemplos prácticos con Python y ASP.NET Core para construir aplicaciones impulsadas por IA.

Programação de Grandes Modelos de Linguagem com Azure OpenAI é um eText prático e atualizado que explora como os grandes modelos de linguagem (LLMs) estão transformando o desenvolvimento de software e a interação entre humanos e computadores. Com foco na plataforma Microsoft Azure e nos serviços Azure OpenAI, o conteúdo apresenta conceitos fundamentais, técnicas avançadas de engenharia de prompts e abordagens modernas para criar aplicações inteligentes capazes de compreender e gerar linguagem natural.

Ao longo deste eText, os leitores aprendem a desenvolver soluções baseadas em IA generativa, desde assistentes pessoais e interfaces conversacionais até aplicações empresariais que utilizam recuperação aumentada por geração (RAG) para interagir com dados e documentos. Com exemplos práticos em Python e ASP.NET Core, o conteúdo oferece uma combinação equilibrada de teoria, arquitetura e implementação, tornando-se um recurso valioso para desenvolvedores, arquitetos de software, profissionais de nuvem e entusiastas de inteligência artificial que desejam aplicar LLMs em cenários reais de negócio.

Introdução aos conceitos fundamentais dos grandes modelos de linguagem (LLMs). Exploração prática do Azure OpenAI e seus principais recursos. Técnicas essenciais e avançadas de engenharia de prompts. Aplicação de estruturas modernas como LangChain e Semantic Kernel. Estratégias para desenvolver assistentes pessoais baseados em IA. Implementação de soluções de conversação utilizando linguagem natural. Boas práticas relacionadas à segurança, privacidade e confiabilidade dos resultados. Desenvolvimento de aplicações com Retrieval-Augmented Generation (RAG). Exemplos práticos utilizando Python e ASP.NET Core. Casos de uso empresariais para automação, produtividade e interação com dados.

Programación de modelos de lenguaje de gran tamaño con Azure OpenAI es un eText práctico y actualizado que explora cómo los modelos de lenguaje de gran tamaño (LLM) están redefiniendo el desarrollo de software moderno y la forma en que las personas interactúan con la inteligencia artificial. Basado en tecnologías como Azure OpenAI y las capacidades que impulsan herramientas similares a ChatGPT, este eText guía a los lectores a través de los conceptos fundamentales de los LLM, la ingeniería de prompts y las mejores prácticas para diseñar aplicaciones inteligentes capaces de comprender, generar y utilizar lenguaje natural de forma eficaz.

A medida que avanza el contenido, los lectores descubrirán cómo crear soluciones empresariales impulsadas por IA, desde asistentes personales y experiencias conversacionales hasta aplicaciones que integran datos propios mediante técnicas de Generación Aumentada por Recuperación (RAG). Con ejemplos prácticos, marcos modernos como LangChain y Semantic Kernel, y casos de uso basados en Microsoft Azure, este eText ofrece una combinación equilibrada de fundamentos teóricos y aplicación práctica para profesionales que buscan aprovechar el potencial de la IA generativa en entornos reales.

Introducción a los fundamentos de los modelos de lenguaje de gran tamaño (LLM). Comprensión de la evolución y el impacto de la IA generativa en el desarrollo de software. Técnicas básicas y avanzadas de ingeniería de prompts. Aplicación de patrones de programación conversacional para escenarios empresariales. Uso de marcos modernos como LangChain y Microsoft Semantic Kernel. Implementación de soluciones basadas en Azure OpenAI y servicios de Microsoft Azure. Estrategias para integrar LLM con datos empresariales mediante RAG. Buenas prácticas para abordar seguridad, privacidad, precisión y uso responsable de la IA. Desarrollo de asistentes virtuales e interfaces de usuario conversacionales. Ejemplos prácticos con Python y ASP.NET Core para construir aplicaciones impulsadas por IA.