Data Handling and Validation with Pydantic
When building APIs, especially for machine learning models, ensuring the data received is correct and structured is essential. Garbage in, garbage out applies directly; invalid input data can lead to prediction errors or application crashes. FastAPI uses Pydantic to handle data validation effectively and declaratively.
This chapter focuses on using Pydantic models within FastAPI. You will learn how to:
- Define data schemas using Pydantic's type annotations.
- Automatically validate incoming request data (like JSON bodies).
- Specify and validate the format of outgoing response data.
- Handle and validate path and query parameters.
- Apply constraints and structure complex data models suitable for typical ML inputs and outputs.
By the end of this chapter, you'll be able to create API endpoints that robustly handle data, ensuring the information passed to your ML models adheres to the expected format and types.
Sections
2.1 Introduction to Pydantic
2.2 Defining Data Models
2.3 Request Body Validation
2.4 Response Model Definition
2.5 Handling Path and Query Parameters
2.6 Data Conversion and Constraints
2.7 Structuring Complex Data Models
2.8 Hands-on Practical: Validating ML Input Data
© 2025 ApX Machine Learning