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ONNX Runtime

ONNX Runtime is a high-performance inference engine for machine learning models in the Open Neural Network Exchange (ONNX) format, enabling cross-platform deployment across various hardware and software environments. It optimizes models for speed and efficiency, supporting a wide range of frameworks like PyTorch, TensorFlow, and scikit-learn through ONNX conversion. Developers use it to deploy AI models in production with low latency and high throughput.

Also known as: onnxruntime, ORT, ONNX Runtime Engine, Microsoft ONNX Runtime, onnx-runtime
🧊Why learn ONNX Runtime?

Developers should learn ONNX Runtime when they need to deploy machine learning models efficiently across multiple platforms, such as cloud, edge devices, or mobile applications, as it provides hardware acceleration and interoperability. It is particularly useful for scenarios requiring real-time inference, like computer vision or natural language processing tasks, where performance and consistency are critical. Using ONNX Runtime reduces deployment complexity by standardizing models into a single format, avoiding framework lock-in.

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