Nudel

NudEL is a fictional term used in this article to describe a hypothetical framework for machine-learning research. It stands for Neural Data Exchange and Explainability Language and is conceived as a modular, language-agnostic system to standardize the representation and exchange of neural network data, explanations, and experimental configurations.

Overview

NudEL envisions a core grammar and a set of extensions that enable teams to serialize models, inputs, outputs, intermediate representations, and explainability traces in a common format. The goal is to improve reproducibility, interoperability, and auditability across tools and platforms. The framework is intended to support both local experiments and cross-system workflows, providing a single reference for data and explanation artifacts.

Architecture and components

The core includes a formal specification of data models for tensors, graphs, metadata, and explanation artifacts. It provides adapters to integrate with popular ML frameworks and an explanation engine capable of producing local and global explanations in a consistent manner. A NudEL runtime can execute NudEL documents, while a NudEL registry stores reusable components and explainers. The architecture emphasizes modularity, versioning, and security considerations to facilitate collaboration across teams.

Data formats and languages

NudEL favors JSON-based and YAML-based serializations and defines a compact, queryable syntax to request explanations, traces, or intermediate results. It emphasizes clear schema definitions, backward compatibility, and extensibility to accommodate new explanation techniques and data representations.

Development and reception

NudEL originated in a hypothetical research proposal and has limited real-world uptake, with prototypes used primarily in educational or demonstration contexts. Critics note that real adoption would require consensus on data models and governance; supporters argue it would lower integration barriers for explainable AI workflows. Related topics include explainable AI, model cards, data interchange formats, and model registries.