Building Generative AI Services with FastAPI (Second Early Release)

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 (Second Early Release) Fast Download
Building Generative AI Services with FastAPI (Second Early Release) Full Download