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## Preview staged site [here](https://sayanshaw24.github.io/onnxruntime/docs/extensions/). **TODO**: In the future, make PR to add/update descriptions for the following new/deprecated ops: **NLP Ops** - BlingFireSentenceBreaker - BpeTokenizer **String Ops** - StringSplit - StringUpper - StringLower - StringECMARegexSplitWithOffsets - StringRaggedTensorToDense - StringMapping **Math Ops** - Inverse - NegPos - SegmentExtraction - SegmentSum **Tensor Ops** - RaggedTensorToSparse - RaggedTensorToDense --------- Co-authored-by: Sayan Shaw <sayanshaw@microsoft.com> Co-authored-by: Nat Kershaw (MSFT) <nakersha@microsoft.com>
44 lines
3.2 KiB
Markdown
44 lines
3.2 KiB
Markdown
---
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title: Ecosystem
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description: See examples of how ONNX Runtime working end to end within the Azure AI and ML landscape and ecosystem
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nav_order: 9
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redirect_from: /docs/tutorials/ecosystem
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---
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# ORT Ecosystem
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{: .no_toc }
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ONNX Runtime functions as part of an ecosystem of tools and platforms to deliver an end-to-end machine learning experience. Below are tutorials for some products that work with or integrate ONNX Runtime.
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## Contents
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{: .no_toc }
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* TOC placeholder
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{:toc}
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## Azure Machine Learning Services
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* [Azure Container Instance: BERT](https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/python/tools/transformers/notebooks/Inference_Bert_with_OnnxRuntime_on_AzureML.ipynb){:target="_blank"}
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* [Azure Container Instance: Facial Expression Recognition](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb){:target="_blank"}
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* [Azure Container Instance: MNIST](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb){:target="_blank"}
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* [Azure Container Instance: Image classification (Resnet)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb){:target="_blank"}
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* [Azure Kubernetes Services: FER+](https://github.com/microsoft/onnxruntime/blob/main/docs/python/inference/notebooks/onnx-inference-byoc-gpu-cpu-aks.ipynb){:target="_blank"}
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* [Azure IoT Sedge (Intel UP2 device with OpenVINO)](https://github.com/Azure-Samples/onnxruntime-iot-edge/blob/master/AzureML-OpenVINO/README.md){:target="_blank"}
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* [Automated Machine Learning](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb){:target="_blank"}
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## Azure Custom Vision
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* [Export a Custom Vision model to ONNX format](https://learn.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/export-programmatically){:target="_blank"}
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* [Use a Custom Vision model with Windows Machine Learning](https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/custom-vision-onnx-windows-ml){:target="_blank"}
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## Azure SQL Edge
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* [ML predictions in Azure SQL Edge and Azure SQL Managed Instance](https://docs.microsoft.com/en-us/azure/azure-sql-edge/deploy-onnx){:target="_blank"}
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## Azure Synapse Analytics
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* [ML predictions in Synapse SQL](https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-predict){:target="_blank"}
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## ML.NET
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* [Automated Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-automl-onnx-model-dotnet?toc=/dotnet/machine-learning/how-to-guides/toc.json&bc=/dotnet/machine-learning/how-to-guides/toc.json){:target="_blank"}
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* [Inference: Object detection](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-onnx){:target="_blank"}
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## NVIDIA Triton Inference Server
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* [ONNX Runtime backend for Triton](https://github.com/triton-inference-server/onnxruntime_backend){:target="_blank"}
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