Millwright: a Rust end-to-end ML framework

Millwright started from the author's experience building ML tooling in Rust, where capable libraries exist but gaps remain between them. Training a model was rarely the problem; the workflow around it—preprocessing, model selection, evaluation, explainability, deployment, and monitoring—often meant integrating several unrelated crates and data representations. The project began as smaller independent crates for missing pieces but evolved into an integration-focused framework.

The current idea is to cover the classical ML lifecycle—ingest, explore, preprocess, select, fit, assess, explain, export, serve, monitor—without reimplementing every algorithm. This approach could simplify Rust-based ML workflows, though it is still at an experimental stage. Its success will depend on how well it bridges the gaps between existing libraries and whether the community adopts it.