Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 7·5 min read
Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool
TL;DR
SMART is a symbolic performance-modeling library for machine learning systems that relies on natural-language design documents instead of traditional code. This innovative approach allows for easy regeneration of implementations, ensuring they remain accurate and up-to-date with minimal technical debt.
✦ Why It Matters
Start using natural-language design documents to streamline your machine learning performance modeling today.
Key Takeaways
- Adopt natural-language design documents for ML performance modeling.
- Utilize AI coding agents to regenerate code from design docs.
- Implement a minimal operator IR for performance analysis.
- Focus on self-documenting practices to reduce technical debt.
How It Works
SMART operates by using a DAG of natural-language design documents that describe the performance modeling process. These documents serve as the primary source for AI coding agents, which regenerate the necessary code based on the provided examples.
The design-doc style emphasizes clarity and step-by-step instructions, making it easier for the agents to understand and implement the required functionality. Additionally, the use of a minimal operator IR allows for efficient performance analysis, enabling both quick assessments and detailed studies.