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This repository contains the recommended ways to train and deploy machine learning models on Azure. It ranges from running massively parallelhyperparameter tuning using Hyperdrive to deploying deep learning models onKubernetes. Eachtutorial takes you step by step through the process to train or deploy your model. If you are confused about what service to use and when look at theFAQ below.

For further documentation on the reference architectures please lookhere.

This repository is arranged as submodules and therefore you can either pull all the tutorials or simply the ones you want.To pull all the tutorials simply run:

git clone --recurse-submodules https://github.com/Microsoft/AIReferenceArchitectures.git

if you have git older than 2.13 run:

git clone --recursive https://github.com/Microsoft/AIReferenceArchitectures.git

Tutorial	Environment	Description	Status
Deploy Deep Learning Model on Kubernetes	Python GPU	Deploy image classification model on Kubernetes or IoT Edge forreal-time scoring using Azure ML
Deploy Classic ML Model on Kubernetes	Python CPU	Train LightGBM model locally using Azure ML, deploy on Kubernetes or IoT Edge forreal-time scoring
Hyperparameter Tuning of Classical ML Models	Python CPU	Train LightGBM model locally and run Hyperparameter tuning using Hyperdrive in Azure ML
Deploy Deep Learning Model on Pipelines	Python GPU	Deploy PyTorch style transfer model forbatch scoring using Azure ML Pipelines
Deploy Classic ML Model on Pipelines	Python CPU	Deploy one-class SVM forbatch scoring anomaly detection using Azure ML Pipelines
Deploy R ML Model on Kubernetes	R CPU	Deploy ML model forreal-time scoring on Kubernetes
Deploy R ML Model on Batch	R CPU	Deploy forecasting model forbatch scoring using Azure Batch and doAzureParallel
Deploy Spark ML Model on Databricks	Spark CPU	Deploy a classification model forbatch scoring using Databricks
Train Distributed Deep Leaning Model	Python GPU	Distributed training of ResNet50 model using Batch AI

The tutorials have been mainly tested on Linux VMs in Azure. Each tutorial may have slightly different requirements such as GPU for some of the deep learning ones. For more details please consult the readme in each tutorial.

Please report issues with each tutorial in the tutorial's own github page.

If there is a particular scenario you are interested in seeing a tutorial for please fill in ascenario suggestion

We are constantly developing interesting AI reference architectures using Microsoft AI Platform. Some of the ongoing projects include IoT Edge scenarios, model scoring on mobile devices, add more... To follow the progress and any new reference architectures, please go to the AI section of thislink.

This project welcomes contributions and suggestions. Most contributions require you to agree to aContributor License Agreement (CLA) declaring that you have the right to, and actually do, grant usthe rights to use your contribution. For details, visithttps://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to providea CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructionsprovided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted theMicrosoft Open Source Code of Conduct.For more information see theCode of Conduct FAQ orcontactopencode@microsoft.com with any additional questions or comments.