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articles/machine-learning/service/how-to-build-deploy-onnx.md

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# ONNX and Azure Machine Learning: Create and deploy interoperable AI models
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The [Open Neural Network Exchange](http://onnx.ai) (ONNX) format is an open standard for representing machine learning models. ONNX is supported by a [community of partners](http://onnx.ai/supported-tools), including Microsoft, who create compatible frameworks and tools. Microsoft is committed to open and interoperable AI so that data scientists and developers can:
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The [Open Neural Network Exchange](https://onnx.ai) (ONNX) format is an open standard for representing machine learning models. ONNX is supported by a [community of partners](https://onnx.ai/supported-tools), including Microsoft, who create compatible frameworks and tools. Microsoft is committed to open and interoperable AI so that data scientists and developers can:
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+ Use the framework of their choice to create and train models
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+ Deploy models cross-platform with minimal integration work
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## More info
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Learn more about ONNX or contribute to the project:
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+ [ONNX project website](http://onnx.ai)
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+ [ONNX project website](https://onnx.ai)
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+ [ONNX code on GitHub](https://github.com/onnx/onnx)

articles/machine-learning/service/how-to-vscode-tools.md

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1. Launch VS Code.
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1. In a browser, visit: http://aka.ms/vscodetoolsforai.
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1. In a browser, visit: https://aka.ms/vscodetoolsforai.
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1. In that web page, click **Install**.
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articles/machine-learning/service/index.yml

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- html: <a href="tutorial-auto-train-models">Let us auto-generate and auto-tune a model for you</a>
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- html: <a href="tutorial-deploy-models-with-aml">Deploy a model as a web service</a>
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- type: paragraph
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text: '<b>Sample notebooks</b>: Explore Azure Machine Learning with <a href="http://aka.ms/aml-notebooks">these Jupyter notebooks</a>, available on GitHub.'
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text: '<b>Sample notebooks</b>: Explore Azure Machine Learning with <a href="https://aka.ms/aml-notebooks">these Jupyter notebooks</a>, available on GitHub.'
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- title: Reference
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items:
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- type: list
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style: unordered
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items:
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- html: <a href="http://aka.ms/data-prep-sdk">Azure Machine Learning Data Prep SDK</a>
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- html: <a href="http://aka.ms/aml-sdk">Azure Machine Learning SDK for Python (main SDK)</a>
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- html: <a href="https://aka.ms/data-prep-sdk">Azure Machine Learning Data Prep SDK</a>
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- html: <a href="https://aka.ms/aml-sdk">Azure Machine Learning SDK for Python (main SDK)</a>
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- html: <a href="https://aka.ms/aml-monitoring-sdk">Azure Machine Learning Monitoring SDK</a>
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- title: Other Azure Machine Learning Technologies

articles/machine-learning/service/toc.yml

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displayName: architecture
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href: concept-azure-machine-learning-architecture.md
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- name: What's happening to Workbench?
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displayName: desktop
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href: overview-what-happened-to-workbench.md
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- name: Quickstarts
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items:
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items:
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- name: Jupyter notebooks (GitHub)
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displayName: example, examples
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href: http://aka.ms/aml-notebooks
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href: https://aka.ms/aml-notebooks
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- name: Concepts
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items:
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- name: Model management
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- name: Set up development environment 
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items:
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- name: Configure an environment
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displayName: local, remote
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displayName: local, remote, azure notebooks, notebook, jupyter, dsvm
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href: how-to-configure-environment.md
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- name: VS Code Tools for AI
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displayName: Visual Studio Code, vscode
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- name: Train models
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- name: Set up training environments
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displayName: compute target, train
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displayName: compute target, dsvm, Data Science Virtual Machine, batchai, batch ai, local, cluster, aci, container instance, databricks, data lake, lake, hdi, hdinsights
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href: how-to-set-up-training-targets.md
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- name: Access data in training
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href: how-to-access-data.md
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- name: TensorFlow
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href: how-to-train-tensorflow.md
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- name: Tune hyperparameters
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displayName: parameter
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href: how-to-tune-hyperparameters.md
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- name: Track experiments and metrics
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href: how-to-track-experiments.md
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- name: Automate machine learning
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displayName: automl
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displayName: automl, auto ml
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href: how-to-automated-ml.md
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items:
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- name: Configure auto training
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- name: Deploy models
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items:
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- name: Where to deploy models
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displayName: publish
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href: how-to-deploy-and-where.md
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- name: Deploy to ACI
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displayName: Azure Container Instances
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- name: Monitor with Application Insights
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href: how-to-enable-app-insights.md
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- name: Manage resource quotas
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displayName: limits
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href: how-to-manage-quotas.md
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- name: Use the Machine Learning CLI
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displayName: cli, command line, command-line
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displayName: cli, command line, command-line, az,
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href: reference-azure-machine-learning-cli.md
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- name: Reference
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items:
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- name: Machine learning SDK
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href: http://aka.ms/aml-sdk
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displayName: api, core, package
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href: https://aka.ms/aml-sdk
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- name: Data prep SDK
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href: http://aka.ms/data-prep-sdk
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displayName: api, preparation, package
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href: https://aka.ms/data-prep-sdk
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- name: Monitoring SDK
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displayName: api, package
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href: https://aka.ms/aml-monitoring-sdk
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- name: Resources
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items:
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- name: Regional availability
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href: https://azure.microsoft.com/regions/services/
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- name: Release notes
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displayName: what's new
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href: azure-machine-learning-release-notes.md
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- name: Known issues
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href: resource-known-issues.md
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- name: Get support
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href: support-for-aml-services.md
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- name: Export and delete data
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displayName: gdpr
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href: how-to-export-delete-data.md
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- name: Deprecated docs
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items:
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href: how-to-migrate.md
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- name: Export or delete account data (deprecated)
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href: ../desktop-workbench/export-delete-experimentation-model-management-data-dsr.md
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- name: Workbench quickstart
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- name: Workbench quickstart (deprecated)
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href: ../desktop-workbench/quickstart-installation.md
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- name: 'Tutorial 1: Prepare data'
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href: ../desktop-workbench/tutorial-classifying-iris-part-1.md

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