ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring). A backend for using ONNX models in TensorFlow can however be found under the ONNX GitHub project. In version 1.10, the ONNX IR comes fitted with an Optional and a SparseTensor type, and has learned to include a list of model local functions in model protos — which is a format for bundling models, graphs, and metadata.