Intermediate

API Documentation

Well-written API documentation is the difference between developers loving your ML service and abandoning it in frustration. Learn to create docs that make integration easy, debugging fast, and adoption smooth.

What ML API Docs Need

ML API documentation shares many requirements with traditional API docs but adds unique challenges: probabilistic outputs, model versioning, confidence scores, and the need to explain what the model does at a conceptual level, not just the endpoint mechanics.

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Key Insight: The best API docs answer three questions immediately: What does this API do? How do I call it? What will I get back? Everything else should be easy to find but not required to get started.

Essential Documentation Components

Component Content
Quick Start A working example in under 5 minutes. Include authentication, a sample request, and expected response.
Endpoint Reference Every endpoint with HTTP method, URL, parameters, request body schema, response schema, and status codes.
Error Handling All error codes with descriptions, common causes, and resolution steps. Include examples of error responses.
Rate Limits & Quotas Request limits, token limits, batch size limits, and how to handle rate limit responses gracefully.

ML-Specific Documentation

  1. Model Behavior Description

    Explain what the model does in plain language. Users need to understand the model's capabilities and limitations before writing any code.

  2. Input Requirements

    Document accepted input formats, size limits, encoding requirements, and preprocessing that users should perform before sending data.

  3. Output Interpretation

    Explain confidence scores, probability distributions, and how to interpret model outputs. Include guidance on setting thresholds for decision-making.

  4. Versioning Strategy

    Document how model versions are managed, how to pin to a specific version, and what changes between versions might break existing integrations.

Documentation Tools

OpenAPI / Swagger

Define your API schema in OpenAPI format to auto-generate interactive documentation, client SDKs, and validation middleware.

Postman Collections

Share runnable API collections that developers can import and execute immediately, with environment variables and test scripts.

Interactive Playgrounds

Provide a web-based playground where users can try the API with their own inputs without writing code or setting up authentication.

Code Examples

Include working code samples in popular languages (Python, JavaScript, curl) for every endpoint and common use case.

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Looking Ahead: In the next lesson, we will explore system design documentation — how to document ML architectures, data flows, and operational procedures.