Official Azure Reference Architectures and Best Practices for AI workloads
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/aiif you have git older than 2.13 run:
git clone --recursive https://github.com/microsoft/ai.gitTo 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
| 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. |
| MLOps | MLOps empowers data scientists and app developers to help bring ML models to production. |
| 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 | |
| Hyperparameter Tuning of Classical ML Models | Python | CPU | Training | Train LightGBM model locally and run Hyperparameter tuning using Hyperdrive in Azure ML | |
| Deploy Deep Learning Model on Pipelines | Python | GPU | Scoring | Deploy PyTorch style transfer model for batch scoring using Azure ML Pipelines | |
| Deploy Classic ML Model on Pipelines | Python | CPU | Scoring | Deploy one-class SVM for batch scoring anomaly detection using Azure ML Pipelines | |
| Deploy R ML Model on Kubernetes | R | CPU | Real-Time Serving | Deploy ML model for real-time scoring on Kubernetes | |
| Deploy R ML Model on Batch | R | CPU | Scoring | Deploy forecasting model for batch scoring using Azure Batch and doAzureParallel | |
| Deploy Spark ML Model on Databricks | Python | Spark | Scoring | Deploy a classification model for batch scoring using Databricks | |
| Train Distributed Deep Leaning Model | Python | GPU | Training | Distributed training of ResNet50 model using Batch AI |
If there is a particular scenario you are interested in seeing a tutorial for please fill in a scenario 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 this link.
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