Rust for Machine Learning: Building AI Tools and Data Pipelines in 2026
Why Rust for Machine Learning?
Yesterday, I started thinking about building a keyword spell checker for Arabic and contributing to المعلم القرانئ project. This led me down a rabbit hole: most modern ML infrastructure is built in Rust for a reason—performance, memory safety, and zero-cost abstractions matter when you’re dealing with embeddings and vector databases at scale.
These tools I use daily are built in Rust:
Polars — Lightning-fast dataframe processing. I use it for all data preprocessing pipelines. It’s not just fast; the API forces you to think about your operations differently than pandas.
Qdrant — My primary vector database. The fact that it’s written in Rust means I can deploy it efficiently and trust the memory model for high-throughput search.
Candle + Fast-Falid — Rust inference engines and late interaction indexing. Understanding how these work at the systems level reveals why Rust is the right choice for production ML infrastructure.
There’s a pattern here: Rust isn’t just “a better language.” It forces architects to make choices visible—memory layout, concurrency models, error handling. This is exactly what ML engineers need to understand.
Learning from First Principles: أحمد فرغل’s رست للغلابة Series
I found Ahmed Farghly’s Arabic Rust course, and it’s been transformative. Each video is ~3 hours of deep systems thinking. I’ve attempted Rust twice before—read the official book years ago—but something didn’t click until now.
What makes this series special: it’s not teaching “Rust syntax.” It’s teaching systems thinking through Rust. Ahmed explains how Operating System concepts—memory layout, alignment, allocators—become visible in Rust code.
Example from video 1: When you define a struct like this:
struct Data {
a: u8,
b: i32
}He doesn’t just say “it compiles.” He shows you:
- Why the order doesn’t matter to Rust (alignment rules)
- How the compiler pads the struct in memory
- Why this is different from C (where order matters)
- What system calls happen under the hood
- How this connects to heap vs stack allocation
- Why Rust has macros at all (they’re solving real compiler problems)
Concepts that seemed abstract before—lifetimes, borrowing, ownership—suddenly make sense because you understand the memory model underneath. That’s the key: Rust semantics emerge from lower-level realities, not arbitrary restrictions.
This series is rare—especially for Arabic speakers. ⭐⭐⭐
ML Projects in Rust: From Learning to Building
Once I’ve built a foundation, here’s what I’m planning to build:
1. Documentation + Semantic Search — Rust documentation is leagues ahead of Python’s. I want to build a tool that embeds Rust docs with static embeddings and makes code-aware search actually useful.
2. Lialia — Arabic keyword spell checker with lexical search. This bridges my native language and ML research.
3. Semantic Search Stack — The big ones:
- Pyversity in Rust — Rebuilding my Python semantic search library in Rust for production deployment
- Semhash + Qdrant — Semantic hashing combined with vector search is showing up in my GSC data; I want to understand the implementation depth
- BM25 Rust — Hybrid retrieval combining lexical (BM25) and semantic search
4. DSPY-powered Tools — RSS reader that uses DSPY for reasoning over feeds
5. Mgrep — Semantic grep for Rust codebases
The pattern: each project forces me to understand both the ML theory and how systems software actually works. That’s the point.
References
Internal Resources
If you’re interested in more about Rust, AI engineering, and my research, explore these sections: