Building Generative Ai Services With Fastapi
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Building Generative AI Services with FastAPI
Ready to build applications using generative AI? This practical book outlines the process necessary to design and build production grade AI services with a FastAPI web server that communicate seamlessly with databases, payment systems, and external APIs. You'll learn how to develop autonomous generative AI agents that stream outputs in real-time and interact with other models. Web developers, data scientists, and DevOps engineers will learn to implement end-to-end production-ready services that leverage generative AI. You'll learn design patterns to manage software complexity, implement FastAPI lifespan for AI model integration, handle long-running generative tasks, perform content filtering, cache outputs, implement retrieval augmented generation (RAG) with a vector database, implement usage/cost monitoring and tracking, protect services with your own authentication and authorization mechanisms, and effectively control stream outputs directly from GenAI models. You'll explore efficient testing methods for AI outputs, validation against databases, and deployment patterns using Docker for robust microservices in the cloud. Build generative services that interact with databases, external APIs, and more Learn how to load AI models into a FastAPI lifecycle memory Monitor and log model requests and responses within services Use authentication and authorization patterns hooked with generative models Handle and cache long-running inference tasks Stream model outputs via streaming events and WebSockets into browsers or files Automate the retraining process of generative models by exposing event-driven endpoints Ali Parandeh is a Chartered Engineer with the UK Engineering Council and a Microsoft and Google certified developer, data engineer, and data scientist.
Building Generative AI Services with FastAPI
Author: Alireza Parandeh
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2025-04-15
Ready to build production-grade applications with generative AI? This practical guide takes you through designing and deploying AI services using the FastAPI web framework. Learn how to integrate models that process text, images, audio, and video while seamlessly interacting with databases, filesystems, websites, and APIs. Whether you're a web developer, data scientist, or DevOps engineer, this book equips you with the tools to build scalable, real-time AI applications. Author Alireza Parandeh provides clear explanations and hands-on examples covering authentication, concurrency, caching, and retrieval-augmented generation (RAG) with vector databases. You'll also explore best practices for testing AI outputs, optimizing performance, and securing microservices. With containerized deployment using Docker, you'll be ready to launch AI-powered applications confidently in the cloud. Build generative AI services that interact with databases, filesystems, websites, and APIs Manage concurrency in AI workloads and handle long-running tasks Stream AI-generated outputs in real time via WebSocket and server-sent events Secure services with authentication, content filtering, throttling, and rate limiting Optimize AI performance with caching, batch processing, and fine-tuning techniques Visit the Book's Website.
FastAPI for Generative AI
Author: Drake Duncan
language: en
Publisher: Independently Published
Release Date: 2025-06-08
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.