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{ "item_title" : "FastAPI for Generative AI", "item_author" : [" Drake Duncan "], "item_description" : "FastAPI for Generative AI: Build and Deploy Scalable AI Applications with PythonUnlock the power of FastAPI, Python, and Generative AI to build real-world, scalable applications that deliver blazing-fast performance and intelligent results. Whether you're integrating LLMs, diffusion models, or deploying AI APIs to production, this comprehensive guide walks you through every step with clear code, best practices, and hands-on projects.This is the definitive guide for developers, machine learning engineers, and backend architects building AI-powered web services using FastAPI.What You'll LearnBuild RESTful and WebSocket-based APIs optimized for AI modelsServe text-generation and image-generation models using FastAPI and PythonHandle asynchronous processing, background tasks, and streaming outputsSecure endpoints with OAuth2, JWT tokens, and role-based access control (RBAC)Use Docker, GitHub Actions, and Render/Fly.io for full CI/CD deploymentsIntegrate with Hugging Face Transformers, Diffusers, and modern AI librariesDevelop a complete multi-model chat and image web app with frontend integration1. Build Scalable AI APIs with FastAPI and PythonLearn how to structure high-performance endpoints for machine learning workloads using FastAPI's async architecture.2. Serve Generative Models Like GPT and Stable DiffusionDeploy language and image models using Hugging Face libraries, optimized for real-world inference.3. Stream Responses with WebSockets and Server-Sent EventsDeliver token-by-token LLM responses and real-time image generation feedback using FastAPI's async capabilities.4. Secure Production-Grade AI EndpointsImplement authentication, rate limiting, and logging for mission-critical AI applications.5. Deploy Your AI App with Docker, CI/CD, and Cloud PlatformsUse containerization and GitHub Actions to launch to Render, Fly.io, or AWS.6. Integrate Frontend Interfaces Using Streamlit or ReactConnect user-friendly frontends to your AI backend for real-time interaction and demo-ready delivery.7. Real-World Project: Generative AI Chat + Image AppFollow a complete walkthrough of building a multi-modal generative AI app, from architecture to deployment.Who This Book Is ForBackend developers building intelligent APIsAI engineers deploying LLMs or diffusion models in productionPython developers exploring modern web frameworksMLOps professionals scaling generative AI systemsTeams building AI SaaS platforms, agentic tools, or custom inference endpointsUnlike generic AI or FastAPI books, FastAPI for Generative AI focuses specifically on real-time generative workloads, delivering both depth and practicality. You'll not only learn how to serve models-you'll learn how to build robust, deployable products around them.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/828/727/9798287277765_b.jpg", "price_data" : { "retail_price" : "17.00", "online_price" : "17.00", "our_price" : "17.00", "club_price" : "17.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
FastAPI for Generative AI|Drake Duncan

FastAPI for Generative AI : Build and Deploy Scalable AI Applications with Python

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Overview

FastAPI for Generative AI: Build and Deploy Scalable AI Applications with Python

Unlock the power of FastAPI, Python, and Generative AI to build real-world, scalable applications that deliver blazing-fast performance and intelligent results. Whether you're integrating LLMs, diffusion models, or deploying AI APIs to production, this comprehensive guide walks you through every step with clear code, best practices, and hands-on projects.

This is the definitive guide for developers, machine learning engineers, and backend architects building AI-powered web services using FastAPI.

What You'll Learn
  • Build RESTful and WebSocket-based APIs optimized for AI models

  • Serve text-generation and image-generation models using FastAPI and Python

  • Handle asynchronous processing, background tasks, and streaming outputs

  • Secure endpoints with OAuth2, JWT tokens, and role-based access control (RBAC)

  • Use Docker, GitHub Actions, and Render/Fly.io for full CI/CD deployments

  • Integrate with Hugging Face Transformers, Diffusers, and modern AI libraries

  • Develop a complete multi-model chat and image web app with frontend integration


1. Build Scalable AI APIs with FastAPI and Python

Learn how to structure high-performance endpoints for machine learning workloads using FastAPI's async architecture.

2. Serve Generative Models Like GPT and Stable Diffusion

Deploy language and image models using Hugging Face libraries, optimized for real-world inference.

3. Stream Responses with WebSockets and Server-Sent Events

Deliver token-by-token LLM responses and real-time image generation feedback using FastAPI's async capabilities.

4. Secure Production-Grade AI Endpoints

Implement authentication, rate limiting, and logging for mission-critical AI applications.

5. Deploy Your AI App with Docker, CI/CD, and Cloud Platforms

Use containerization and GitHub Actions to launch to Render, Fly.io, or AWS.

6. Integrate Frontend Interfaces Using Streamlit or React

Connect user-friendly frontends to your AI backend for real-time interaction and demo-ready delivery.

7. Real-World Project: Generative AI Chat + Image App

Follow a complete walkthrough of building a multi-modal generative AI app, from architecture to deployment.


Who This Book Is For
  • Backend developers building intelligent APIs

  • AI engineers deploying LLMs or diffusion models in production

  • Python developers exploring modern web frameworks

  • MLOps professionals scaling generative AI systems

  • Teams building AI SaaS platforms, agentic tools, or custom inference endpoints

Unlike generic AI or FastAPI books, FastAPI for Generative AI focuses specifically on real-time generative workloads, delivering both depth and practicality. You'll not only learn how to serve models-you'll learn how to build robust, deployable products around them.

This item is Non-Returnable

Details

  • ISBN-13: 9798287277765
  • ISBN-10: 9798287277765
  • Publisher: Independently Published
  • Publish Date: June 2025
  • Dimensions: 10 x 7 x 0.46 inches
  • Shipping Weight: 0.84 pounds
  • Page Count: 216

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