Building ML framework with Rust and Category Theory

A new working draft presents a machine learning framework built with Rust, leveraging category theory to structure the pipeline of transformations and models. This approach aims to make ML systems more explicit, maintainable, and mathematically grounded, emphasizing practical engineering over abstract theory.

The draft, titled ‘Category Theory for Tiny ML in Rust,’ is a collaborative effort by Hamze Ghalebi and Farzad Jafarranmani. It develops a small, explicit ML system where domain objects are represented as Rust types, and transformations as typed functions, following category theory principles.

The project treats category theory not just as a mathematical abstraction but as an engineering framework, turning theoretical concepts into executable Rust code. The current version is a work in progress, with chapters, examples, and terminology still evolving. The source code and draft are publicly accessible on GitHub, inviting community feedback.

Why It Matters

This development is significant because it bridges advanced mathematical concepts with practical software engineering, potentially leading to more transparent, verifiable, and maintainable ML systems. Using Rust ensures safety and performance, making this approach relevant for production environments where reliability is critical.

It also exemplifies how category theory can be operationalized in software, moving beyond abstract mathematics into concrete engineering tools, which could influence future ML framework design and educational approaches.

Background

Traditional ML frameworks focus on numerical computation and often lack explicit structural representations. Recent efforts in formal methods and mathematical rigor aim to improve system reliability and interpretability. This draft continues that trend by integrating category theory into ML pipeline design, inspired by prior work in denotational semantics and type theory.

The project builds on the idea that ML pipelines can be viewed as compositions of objects and transformations—core concepts in category theory—making the structure of models more explicit and easier to reason about.

“We are turning mathematical and ML ideas into understandable, typed, maintainable systems using Rust and category theory.”

— Hamze Ghalebi

“Applying category theory to ML pipelines allows us to formalize transformations and compositions in a precise, executable way.”

— Farzad Jafarranmani

What Remains Unclear

It is not yet clear how well this approach scales to larger, real-world ML systems or how it compares in performance and usability to existing frameworks. The draft is still evolving, and the effectiveness of the category-theoretic approach remains to be validated through practical implementation and community feedback.

What’s Next

Next steps include refining the draft, expanding examples, and gathering feedback from the community. Further development aims to demonstrate the framework’s applicability to real-world ML tasks and evaluate its advantages over traditional pipelines.

Key Questions

What is the main goal of this project?

The project aims to develop a structured, mathematically grounded machine learning framework in Rust, using category theory as an engineering tool to improve clarity, maintainability, and correctness.

How does category theory benefit machine learning systems?

Category theory provides a formal language for describing objects and transformations, enabling explicit modeling of ML pipelines, which can improve reasoning, composability, and verification of systems.

Is this approach ready for production use?

Not yet. The draft is still in development, and its practical viability, scalability, and performance are under evaluation. Community feedback will influence future iterations.

What role does Rust play in this framework?

Rust provides safety, performance, and expressive type systems, making it suitable for implementing the category-theoretic ML pipeline as a reliable, maintainable software system.