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How to Build a REST API for ML Model Serving in Rust Build a Rust REST API for ML serving using axum and tokio to handle requests and return predictions. How to Build Vector Search in Rust You build vector search in Rust by using the `tantivy` crate, which provides a full-text search engine with vector similarity capabilities. Add the dependency to your `Cargo.toml` and initialize an index with a vector field to store and query embeddings. How to Run ONNX Models in Rust Run ONNX models in Rust by adding the ort crate and using SessionBuilder to load and execute the model file. How to Use burn for Deep Learning in Rust Install the burn crate, define a model struct with the Module derive macro, and call forward to run inference. How to Use Hugging Face Models from Rust Use the huggingface crate in Rust to load and run pre-trained models by adding the dependency and initializing a pipeline. How to Use the candle ML Framework in Rust Use the candle crate to build and run deep learning models in Rust by adding dependencies and initializing tensors. Overview of Machine Learning Libraries in Rust Rust uses external crates like linfa and burn for machine learning instead of a single built-in library.