domain-ml

What problem does it solve?

This Skill provides guidance and best practices for developing Machine Learning and AI applications specifically within the Rust programming language ecosystem.

Core Features & Use Cases

• Framework Selection: Recommends appropriate Rust crates (like candle, burn, tch-rs, tract) based on use cases such as inference, training, or PyTorch model compatibility.
• Design Patterns: Illustrates common patterns for efficient ML development in Rust, including model loading, batching, streaming, and GPU asynchronous operations.
• Use Case: A developer wants to build a Rust application that performs real-time object detection using a pre-trained ONNX model. This Skill would guide them to use tract for ONNX inference and suggest patterns for efficient model loading and input processing.

Quick Start

Use the domain-ml skill to find the recommended Rust crates for building an ML inference engine.