use-ml 0.0.1

Composable machine-learning primitive facade for RustUse.
Documentation

use-ml

Facade crate for the focused machine-learning primitive crates in RustUse.

Experimental

use-ml is experimental while the workspace remains below 0.3.0.

Example

use use_ml::{MlDatasetName, MlFeatureName, MlModelName, TensorShape};

let dataset = MlDatasetName::new("iris")?;
let feature = MlFeatureName::new("sepal_width")?;
let model = MlModelName::new("baseline-classifier")?;
let shape = TensorShape::new([150, 4])?;

assert_eq!(dataset.as_str(), "iris");
assert_eq!(feature.as_str(), "sepal_width");
assert_eq!(model.as_str(), "baseline-classifier");
assert_eq!(shape.rank(), 2);
# Ok::<(), Box<dyn std::error::Error>>(())

Scope

  • Re-export the focused use-ml-* primitive crates.
  • Keep implementation logic inside focused child crates.
  • Provide one dependency for machine-learning metadata primitives.

Relationship to use-ai

use-ml models machine-learning primitives: datasets, features, labels, tensors, model artifacts, training, inference, evaluation, metrics, pipelines, embeddings, experiments, and model documentation.

use-ai models AI interaction primitives: prompts, messages, roles, context windows, tool calls, agents, RAG, reasoning, memory, guardrails, AI model interfaces, and AI-specific evaluation.

These sets are siblings. They should interoperate conceptually but avoid dependency cycles.

Non-goals

  • Training, inference, serving, tensor math, vector search, registry behavior, or experiment tracking.
  • Prompt, chat, agent, RAG, guardrail, or AI-provider interface modeling.

License

Licensed under either Apache-2.0 or MIT.