课程

Deploying AI into Production with FastAPI

高级技能水平

更新时间 2025年11月

Learn how to use FastAPI to develop APIs that support AI models, built to meet real-world demands.

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FastAPIArtificial Intelligence

4小时

14 视频

46 道练习

3,900 XP

4,514

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课程描述

FastAPI for AI Model Deployment

FastAPI is a Python web framework ideal for building APIs, especially for deploying machine learning and AI models. FastAPI’s speed and modularity make it a powerful choice for data scientists and machine learning engineers seeking to integrate AI solutions into web applications.

Building APIs for Models in Production

In this course, you will learn how to build and deploy APIs for model serving using FastAPI. By the end, you’ll create robust API endpoints that handle data input validation, authentication, and error management. Hands-on exercises will guide you through building complete APIs designed to handle AI model interactions.

Securing and Scaling FastAPI Applications

You'll also implement API key authentication, apply custom rate limiting to control request flow, and enhance performance through asynchronous processing. Additionally, you’ll learn to manage API versions, improve input validation for complex data types, and implement monitoring and logging to ensure your application runs robustly in production environments.

先决条件

Introduction to FastAPI Large Language Models (LLMs) Concepts

1

Introduction to FastAPI for Model Deployment

Start serving your ML model's predictions via FastAPI endpoints. You'll learn to load pre-trained ML models and create API endpoints to serve predictions as serialized responses over HTTP requests. You'll leverage Pydantic data models to validate requests and responses.

GET and POST requests for AI

GET endpoint for model information

POST endpoint for model registration

FastAPI prediction with a pre-trained model

Load the pre-trained model

Create the prediction endpoint

Running the FastAPI app

Request and response models

Create a Pydantic model for ML input

Validate request and response for ML prediction

2

Integrating AI Models

Learn how to serve machine learning models through FastAPI endpoints. This chapter covers creating endpoints that return predictions, handling different types of input data, and implementing robust input validation. You'll build production-ready APIs that can validate different types of input data while having ML models loaded at server startup with zero downtime.

Handling different input types in FastAPI

Handle textual request data

Handle numerical request data

Input validation in FastAPI

Field validation

Adding custom validators

Testing custom validators

Loading a pre-trained model

Loading AI model at server startup

Health-check API for model loading

Returning structured prediction response

Returning structured output from API

Testing the endpoint for structured response

3

Securing and Optimizing the API

This chapter covers securing APIs with key-based authentication, managing request rates with custom rate limiting, and improving performance through asynchronous processing. You'll learn to protect endpoints, prevent abuse, and handle time-consuming tasks efficiently, preparing your API for production.

API Key Authentication

Securing APIs with key authentication

Secure the API endpoint

Rate Limiting

Implementing rate limiter

Add rate limiting to endpoint

Asynchronous processing

Create an async sentiment analysis endpoint

Implementing background tasks

Handling timeout errors

4

API Versioning, Monitoring and Logging

This chapter covers advanced topics that will enable you to support FastAPI apps long term in production. Topics include versioning and documenting API endpoints, advanced input validation to support more complex input and output, and monitoring and logging to ensure apps are running correctly and troubleshoot live when they are not.

API versioning and documentation

Classify code changes for new endpoint versions

Adding a new endpoint version

Adding an API description header

Advanced input validation and error handling

Nesting models

Custom model validator

Global exception handler

Monitoring and logging

Logging time to load a model

Adding a simple health check endpoint

Sharing model parameters with monitoring

Deploying AI into Production with FastAPI

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