What is SurrealML: A getting started guide

SurrealML is a tool designed to streamline the hosting, version control, and deployment of machine learning models by integrating with SurrealDB, allowing for model inference using SurrealQL statements. Unlike traditional machine learning libraries focused on model training, SurrealML emphasizes the packaging and deployment of trained models onto SurrealDB instances, enabling users to perform flexible model inferences with simple commands. This flexibility allows for comparisons between different model versions and the integration of inference results into database queries. The guide provides a practical walkthrough for setting up the environment, training models using PyTorch and scikit-learn, and integrating those models with SurrealDB for efficient computation. SurrealML facilitates local and remote model inference in ONNX format, ensuring language agnosticism and potential for future expansion to other language bindings. This approach marks a significant advancement in simplifying machine learning deployment processes, with SurrealQL providing adaptable interaction with multiple deployed models.

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