---
title: ORT Ecosystem
parent: Tutorials
nav_order: 3
---
# ORT Ecosystem
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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.

## Contents
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## Azure Machine Learning Services
* [Azure Container Instance: BERT](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers/notebooks/Inference_Bert_with_OnnxRuntime_on_AzureML.ipynb)
* [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)
* [Azure Container Instance: MNIST](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb)
* [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)
* [Azure Kubernetes Services: FER+](https://github.com/microsoft/onnxruntime/tree/master/docs/python/notebooks/onnx-inference-byoc-gpu-cpu-aks.ipynb)
* [Azure IoT Sedge (Intel UP2 device with OpenVINO)](https://github.com/Azure-Samples/onnxruntime-iot-edge/blob/master/AzureML-OpenVINO/README.md)
* [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)

## Azure Custom Vision
* [Export a Custom Vision model to ONNX format](https://docs.microsoft.com/en-us/samples/azure-samples/cognitive-services-onnx-customvision-sample/cognitive-services-onnx-customvision-sample/)
* [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)

## Azure Live Video Analytics
* [Azure Video Analytics: YOLOv3 and TinyYOLOv3](https://github.com/Azure/live-video-analytics/tree/master/utilities/video-analysis/yolov3-onnx)

## Azure SQL Edge
* [ML predictions in Azure SQL Edge and Azure SQL Managed Instance](https://docs.microsoft.com/en-us/azure/azure-sql-edge/deploy-onnxJ)

## Azure Synapse Analytics
* [ML predictions in Synapse SQL](https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-predict)


## ML.NET
* [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)
* [Inference: Object detection](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-onnx)

## NVIDIA Triton Inference Server
* [ONNX Runtime backend for Triton](https://github.com/triton-inference-server/onnxruntime_backend)