eBooks > Building AI - Powered Distributed Architectures with Aspire

Building AI - Powered Distributed Architectures with Aspire


Date: 10 October 2026 | Posted by: BeMyLove

Building AI - Powered Distributed Architectures with Aspire

Building AI - Powered Distributed Architectures with Aspire | 49.14 MB

Title: Building AI - Powered Distributed Architectures with Aspire
Author: Mehmet Ozkaya
Category: Parallel Processing, Programming, Software Development
Language: English | 354 Pages | ISBN: 9781808492761



Description:

Build cloud-native distributed .NET applications with Aspire, ASP.NET Core microservices, generative AI, semantic search, and Azure deployment.

Key Features

  • Build an end-to-end distributed e-commerce system with AI capabilities
  • Integrate PostgreSQL, Redis, RabbitMQ, Keycloak, and Ollama with Aspire
  • Deploy secure, AI-enabled microservices to Azure Container Apps

    Book Description
    Build practical AI-powered distributed applications by seeing how microservices, messaging, security, data services, and generative AI work together in one project. This book guides you through building a cloud-native e-commerce application with independently deployed services, reliable communication, specialized data stores, and intelligent features.
    You'll use Aspire to orchestrate Catalog and Basket microservices, configure service discovery, and integrate PostgreSQL and Redis. You'll implement synchronous communication and asynchronous messaging with RabbitMQ and MassTransit, secure Basket APIs with Keycloak and JWT bearer tokens, and connect a Blazor frontend to the distributed backend.
    You'll then add generative AI capabilities using Microsoft.Extensions.AI, Microsoft Agent Framework, and Ollama. You'll build customer-support Q&A and semantic product search using embeddings and vector storage before deploying the application to Azure Container Apps.
    Written by software and solutions architect Mehmet Ozkaya, who has more than 15 years of industry experience and has taught more than 200,000 students, this book combines hands-on development with architectural guidance. By the end, you'll be able to design, orchestrate, secure, enhance, and deploy AI-enabled distributed .NET applications.

    What you will learn

  • Design cloud-native distributed systems with Aspire
  • Build Catalog and Basket microservices with ASP.NET Core
  • Orchestrate PostgreSQL and Redis as backing services
  • Implement event-driven messaging with RabbitMQ and MassTransit
  • Secure microservice endpoints with Keycloak and JWT bearer tokens
  • Build enterprise AI features with Microsoft.Extensions.AI, Ollama, and Microsoft Agent Framework
  • Implement semantic search with embeddings and vector storage
  • Deploy distributed applications to Azure Container Apps

    Who this book is for
    .NET developers, software engineers, and solution architects who want to build cloud-native distributed applications and add generative AI capabilities will benefit from this book. It is also suited to developers exploring microservices, event-driven architecture, Aspire, and AI integration. Basic knowledge of C# and programming concepts is required.

    Table of Contents

  • Introducing the AI-Powered eShop Architecture with Aspire
  • Cloud-Native Distributed Architectures and Backing Services
  • Developing Distributed Applications with Aspire
  • Building Your First Aspire Application
  • Building an eShop Distributed Microservices Architecture with Aspire
  • Developing the Catalog Microservice with PostgreSQL and Aspire
  • Developing the Basket Microservice with Redis and Aspire
  • Synchronous Communication Between Catalog and Basket Using Aspire Service Discovery
  • Asynchronous Communication with RabbitMQ and MassTransit in Aspire
  • Securing the Basket Microservice with Keycloak Authentication
  • Developing the Client Blazor Web Application
  • Deploying the eShop Aspire Application to Azure Container Apps
  • Building Generative AI Features with Microsoft.Extensions.AI, Microsoft Agent Framework, and Ollama
  • Building Semantic Product Search with Vector Embeddings


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