Building Generative AI Services with FastAPI (5th Early Release)КНИГИ » ПРОГРАММИНГ
Название: Building Generative AI Services with FastAPI: A Practical Approach to Developing Context Rich Generative AI Applications (5th Early Release) Автор: Ali Parandeh Издательство: O’Reilly Media, Inc. Год: 2024-12-04 Страниц: 398 Язык: английский Формат: epub Размер: 23.0 MB
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.
We use computers to automate solutions to everyday problems. In the past, automating a process required you to manually write code, which could become tedious for complex issues like spam detection. Nowadays, you can develop a model by training it with sufficient data that contains the necessary patterns to understand the nuances of the business process. Once trained, this model can then replace your manually written application code.
This gave rise to a wave of AI-powered applications in the market, solving a range of problems including price optimization, product recommendation or weather forecasting. As part of this wave, generative models emerged that differed from other types of AI in their ability to generate novel outputs rather than just predicting, analyzing or classifying data. As a software engineer, I believe these models have certain capabilities that will drive the roadmap of future applications.
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.
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