How Rust's Type System and a Single Vec<f64> Shrunk an ML Docker Image 400x

A developer building 'datarust', a Rust-based machine learning library, has detailed how a single flat memory structure called Matrix underpins the entire library's efficiency. The library splits data into two strictly typed structures at the source — Matrix for numeric data and StrMatrix for strings — preventing type mismatches at compile time rather than at runtime. This design means errors like mismatched column shapes are caught during construction, not silently passed downstream to corrupt model training. The approach contrasts with Python tools like pandas and scikit-learn, where type mismatches and ragged arrays can go undetected until deep in the processing pipeline. The result is a binary of just 2.3 MB and a Docker image reduced from roughly 900 MB to 8 MB, a roughly 400-fold reduction.