Lilly Tech Systems
AI Documentation
Learn how to create clear, comprehensive documentation for AI systems. From model cards and datasheets to API docs and system design documents, build a documentation practice that promotes transparency, reproducibility, and responsible AI.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why AI documentation matters, who the audience is, and how good docs improve transparency, trust, and reproducibility.
2. Model Cards
Create standardized model cards that describe model purpose, performance, limitations, ethical considerations, and intended use.
3. Datasheets
Document datasets with datasheets covering motivation, composition, collection process, preprocessing, and maintenance plans.
4. API Docs
Write clear API documentation for ML services with request/response schemas, error handling, rate limits, and usage examples.
5. System Design Docs
Create architecture documentation for ML systems covering data flow, model serving, infrastructure, and operational runbooks.
6. Best Practices
Documentation workflows, automation, version control, templates, and building a culture of documentation in AI teams.
What You'll Learn
By the end of this course, you'll be able to:
Write Model Cards
Create comprehensive model cards that communicate model capabilities, limitations, and ethical considerations to all stakeholders.
Document Datasets
Build datasheets that describe dataset provenance, composition, biases, and appropriate use cases for responsible data sharing.
Create API Docs
Write developer-friendly API documentation that makes your ML services easy to integrate, test, and debug.
Design System Docs
Document ML system architecture, data flows, and operational procedures for maintainability and knowledge sharing.