Tools

This page lists tools and libraries for building, training, and deploying machine learning models in the AI/ML in Rust tutorial, leveraging Rust’s performance and safety. Explore the Rust ML ecosystem at arewelearningyet.com.

• Rust Programming Language

  • Purpose: Language for ML tasks.
  • Explanation: Enables fast, safe model development.
    • Cargo: Rust’s package manager, manages ML library dependencies.
  • Open-Source: github.com/rust-lang/rust

• linfa

  • Purpose: Traditional ML algorithms.
  • Explanation: Supports regression, clustering (Core ML).
  • Open-Source: github.com/rust-ml/linfa

• tch-rs

  • Purpose: Deep learning with PyTorch.
  • Explanation: Builds neural networks (Deep Learning).
  • Open-Source: github.com/LaurentMazare/tch-rs

• polars

  • Purpose: Data processing library.
  • Explanation: Enables fast preprocessing (Practical ML Skills).
  • Open-Source: github.com/pola-rs/polars

• nalgebra

  • Purpose: Linear algebra computations.
  • Explanation: Handles matrices (Mathematical Foundations).
  • Open-Source: github.com/dimforge/nalgebra

• rust-bert

  • Purpose: NLP tasks.
  • Explanation: Offers models for sentiment analysis (Advanced Topics).
  • Open-Source: github.com/guillaume-be/rust-bert

• actix-web

  • Purpose: Model deployment.
  • Explanation: Serves models as APIs (Practical ML Skills).
  • Open-Source: github.com/actix/actix-web

Next Steps

Continue to Tutorial Roadmap or start with Setup.