AI Reference Architectures

This repository contains the recommended ways to train and deploy models on Azure. It ranges from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes. Each tutorial takes you step by step through the process to train or deploy your model. The tutorials are set up as Jupyter notebooks for the Python ones and RMarkdown for the R ones so you can simply download them and start running them. For further documentation on the reference architectures please look here.

Getting Started

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

Tutorials

Tutorial	Environment	Description	Status
Deploy Deep Learning Model on Kuberenetes	Python GPU	Deploy image classification model on Kubernetes for real-time scoring
Deploy Classic ML Model on Kubernetes	Python CPU	Deploy LightGBM model on Kubernetes for real-time scoring
Hyperparameter Tuning of Classical ML Models	Python CPU	Run Hyperparameter tuning on LightGBM using Hyperdrive
Deploy Deep Learning Model on Pipelines	Python GPU	Deploy style transfer model for batch scoring using Azure ML Pipelines
Deploy Classic ML Model on Pipelines	Python CPU	Deploy one-class SVM for batch scoring anomaly detection using Azure ML Pipelines

Requirements

The tutorials have been mainly tested on Linux VMs in Azure. They haven't been tested on Windows yet. 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.

Reporting Issues

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

Contributing

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

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

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.