Azure AI Reference Architectures & Best Practices

Official Azure Reference Architectures and Best Practices for AI workloads

Getting Started

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

git clone --recurse-submodules https://github.com/microsoft/ai

if you have git older than 2.13 run:

git clone --recursive https://github.com/microsoft/ai.git

To pull a single submodule (e.g. DeployDeepModelKubernetes) run:

git clone https://github.com/microsoft/ai
cd ai
git submodule init submodules/DeployDeepModelKubernetes
git submodule update

Best Practices

Title	Description
Computer Vision	Accelerate the development of computer vision applications with examples and best practice guidelines for building computer vision systems
Natural Language Processing	State-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language.
Recommenders	Examples and best practices for building recommendation systems, provided as Jupyter notebooks.

Reference Architectures

Title	Language	Environment	Design	Description	Status
Deploy Classic ML Model on Kubernetes	Python	CPU	Real-Time Scoring	Train LightGBM model locally using Azure ML, deploy on Kubernetes or IoT Edge for real-time scoring
Deploy Deep Learning Model on Kubernetes	Python	Keras	Real-Time Scoring	Deploy image classification model on Kubernetes or IoT Edge for real-time scoring using Azure ML

Recommend a Scenario

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

Ongoing Work

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 this link.

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.