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FastAPI for Generative AI: Build and Deploy Scalable AI Applications with Python
FastAPI for Generative 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 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 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 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 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 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 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 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 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 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.
205 pages, Kindle Edition
Published June 9, 2025
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