After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Descrizione del corso
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.Prerequisiti
Curriculum
Programma del corso
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 AI50 XP
- GET endpoint for model information100 XP
- POST endpoint for model registration100 XP
- FastAPI prediction with a pre-trained model50 XP
- Load the pre-trained model100 XP
- Create the prediction endpoint100 XP
- Running the FastAPI app100 XP
- Request and response models50 XP
- Create a Pydantic model for ML input100 XP
- Validate request and response for ML prediction100 XP
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.
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.
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.
Deploying AI into Production with FastAPI
Corso
completato
