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From Data Science to Production: Generating API Documentation with Swagger
Latest   Machine Learning

From Data Science to Production: Generating API Documentation with Swagger

Last Updated on March 7, 2024 by Editorial Team

Author(s): Wencong Yang

Originally published on Towards AI.

Figure 1. Integrate an AI model into an application. Source: by author.

In the realm of IT application development, especially as a data scientist, it’s customary to encapsulate data processing and model inference pipelines into an API service. This API service essentially acts as a URL endpoint for invoking your AI model. This facilitates integration of your model into various applications by other engineers such as architects, software engineers, and web developers. However, to effectively collaborate, these engineers require detailed documentation covering essential aspects:

URL path to call the modelRequired parameters for model inferenceData structure of the API responseQuick methods for API trial or testing

Writing comprehensive documentation manually can be tedious. Fortunately, tools like Swagger UI offer automated generation of interactive API documentation. Key features include:

Automatic Documentation: Generate documentation directly from your application code.Interactive Interface: Swagger UI provides a user-friendly interface for exploring and testing APIs.Clear Structure: Organize API descriptions, define data schemas, and showcase query parameters and responses effectively.

This article aims to provide a tutorial on utilizing Swagger UI for Python API services, specifically those written with the Tornado web framework. Additionally, it demonstrates the application of Swagger UI in a real-world data science project scenario. For readers unfamiliar with building… Read the full blog for free on Medium.

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