onnxruntime/docs/ecosystem/index.md

title	description	nav_order	redirect_from
Ecosystem	See examples of how ONNX Runtime working end to end within the Azure AI and ML landscape and ecosystem	9	/docs/tutorials/ecosystem

ORT Ecosystem

{: .no_toc }

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

{: .no_toc }

• TOC placeholder {:toc}

Azure Machine Learning Services

• Azure Container Instance: BERT{:target="_blank"}
• Azure Container Instance: Facial Expression Recognition{:target="_blank"}
• Azure Container Instance: MNIST{:target="_blank"}
• Azure Container Instance: Image classification (Resnet){:target="_blank"}
• Azure Kubernetes Services: FER+{:target="_blank"}
• Azure IoT Sedge (Intel UP2 device with OpenVINO){:target="_blank"}
• Automated Machine Learning{:target="_blank"}

Azure Custom Vision

• Export a Custom Vision model to ONNX format{:target="_blank"}
• Use a Custom Vision model with Windows Machine Learning{:target="_blank"}

Azure SQL Edge

• ML predictions in Azure SQL Edge and Azure SQL Managed Instance{:target="_blank"}

Azure Synapse Analytics

• ML predictions in Synapse SQL{:target="_blank"}

ML.NET

• Automated Machine Learning{:target="_blank"}
• Inference: Object detection{:target="_blank"}

NVIDIA Triton Inference Server

• ONNX Runtime backend for Triton{:target="_blank"}