NEOPOLIS AKADEMY
Deploying AI into Production with FastAPI
Learn to deploy robust AI APIs using FastAPI and Pydantic. The course addresses securing, optimizing, versioning and monitoring AI endpoints, with local hands-on media and sequential activities to build and maintain production services.
View this course on Akademy ↗Enrolment and practical details are available on Neopolis Akademy.
What you will explore
This course shows how to turn AI models into usable APIs using FastAPI and Pydantic, focusing on reliability and maintainability.
It combines hands-on examples with guidance on security configuration (authentication, secret management), performance tuning and API versioning strategies.
Monitoring and observability are covered to detect model drift and latency issues, including integration of logging and metrics tools.
STEP BY STEP
Course programme
01Deploying AI into Production with FastAPI
Practical guide to deploying AI models with FastAPI: creating GET/POST endpoints for model info and registration, handling diverse input types, key-based security, rate limiting, and API versioning and monitoring.
Demonstrates using FastAPI to expose models: GET endpoints for model metadata, POST endpoints for model registration, and prediction calls with a pre-trained model.
Covers integrating models by handling different input types (textual and numeric) and validating request data for robust endpoints.
Discusses securing and optimizing APIs: key-based authentication, protecting endpoints, rate limiting, and API versioning plus monitoring and logging practices.
Explore this module on Akademy ↗Programme source: Neopolis Akademy. Original course page