Machine learning and AI applications in Rust with tensor operations, model inference, and GPU acceleration.

• Covers tensor libraries (ndarray), inference frameworks (tract for ONNX, candle, burn), and PyTorch bindings (tch-rs) for training and deployment workflows
• Emphasizes memory efficiency through zero-copy operations, GPU batching, and standard model formats (ONNX) for portability across Python and Rust
• Provides design patterns for model loading with lazy initialization, batched inference for GPU throughput, and async data pipelines to prevent GPU idle time
• Includes code examples for inference servers and batched prediction, plus common pitfalls like tensor cloning and per-request model loading