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| 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: 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"}