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# AI Architectures & Practices
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Official Azure Reference Architectures and Best Practices for AI workloads
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# Architectures <a name="Architectures"></a>
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| Title | Language | Environment | Design | Description | Status |
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|----------------------------------------------|-------------|-------------|-------------|-----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [Deploy Classic ML Model on Kubernetes](https://github.com/Microsoft/MLAKSDeployAML) | Python | CPU | Real-Time Scoring| Train LightGBM model locally using Azure ML, deploy on Kubernetes or IoT Edge for _real-time_ scoring | [](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_build/latest?definitionId=21&branchName=master)
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| [Deploy Deep Learning Model on Kubernetes](https://github.com/Microsoft/AKSDeploymentTutorialAML) | Python | Keras | Real-Time Scoring| Deploy image classification model on Kubernetes or IoT Edge for _real-time_ scoring using Azure ML | [](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_build/latest?definitionId=17&branchName=master)
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# Practices <a name="Practices"></a>
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| Title | Description |
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|-------|-------------|
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|[Computer Vision](https://github.com/microsoft/computervision)| Accelerate the development of computer vision applications with examples and best practice guidelines for building computer vision systems
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|[Nature Language Processing](https://github.com/microsoft/nlp)|State-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language.|
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|[Recommenders](github.com/microsoft/recommenders)| Examples and best practices for building recommendation systems, provided as Jupyter notebooks.|
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## Recommend a Scenario
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If there is a particular scenario you are interested in seeing a tutorial for please fill in a [scenario suggestion](https://github.com/Microsoft/AIReferenceArchitectures/issues/new?assignees=&labels=&template=scenario_request.md&title=%5BSCENARIO%5D)
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@ -17,18 +17,12 @@ if you have git older than 2.13 run:
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git clone --recursive https://github.com/Microsoft/AIReferenceArchitectures.git
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```
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# Tutorials <a name="tutorials"></a>
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| Tutorial | Environment | Description | Status |
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|----------------------------------------------|-------------|-----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [Deploy Deep Learning Model on Kubernetes](https://github.com/Microsoft/AKSDeploymentTutorialAML) | Python GPU | Deploy image classification model on Kubernetes or IoT Edge for _real-time_ scoring using Azure ML | [](https://dev.azure.com/customai/AKSDeploymentTutorialAML/_build/latest?definitionId=11&branchName=master) |
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| [Deploy Classic ML Model on Kubernetes](https://github.com/Microsoft/MLAKSDeployAML) | Python CPU | Train LightGBM model locally using Azure ML, deploy on Kubernetes or IoT Edge for _real-time_ scoring |  |
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| [Hyperparameter Tuning of Classical ML Models](https://github.com/Microsoft/MLHyperparameterTuning) | Python CPU | Train LightGBM model locally and run Hyperparameter tuning using Hyperdrive in Azure ML |  |
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| [Deploy Deep Learning Model on Pipelines](https://github.com/Azure/Batch-Scoring-Deep-Learning-Models-With-AML) | Python GPU | Deploy PyTorch style transfer model for _batch_ scoring using Azure ML Pipelines | [](https://dev.azure.com/customai/BatchScoringDeepLearningModelsWithAMLPipeline/_build/latest?definitionId=9&branchName=master) |
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| [Deploy Classic ML Model on Pipelines](https://github.com/Microsoft/AMLBatchScoringPipeline) | Python CPU | Deploy one-class SVM for _batch_ scoring anomaly detection using Azure ML Pipelines |  |
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| [Deploy R ML Model on Kubernetes](https://github.com/Azure/RealtimeRDeployment) | R CPU | Deploy ML model for _real-time_ scoring on Kubernetes | |
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| [Deploy R ML Model on Batch](https://github.com/Azure/RBatchScoring) | R CPU | Deploy forecasting model for _batch_ scoring using Azure Batch and doAzureParallel | |
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| [Deploy Spark ML Model on Databricks](https://github.com/Azure/BatchSparkScoringPredictiveMaintenance) | Spark CPU | Deploy a classification model for _batch_ scoring using Databricks | |
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| [Train Distributed Deep Leaning Model](https://github.com/Azure/DistributedDeepLearning/) | Python GPU | Distributed training of ResNet50 model using Batch AI | |
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# Architectures <a name="Architectures"></a>
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| Architectures | Language | Environment | Design | Description | Status |
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|----------------------------------------------|-------------|-------------|-------------|-----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [Deploy Classic ML Model on Kubernetes](https://github.com/Microsoft/MLAKSDeployAML) | Python | CPU | Real-Time Scoring| Train LightGBM model locally using Azure ML, deploy on Kubernetes or IoT Edge for _real-time_ scoring | [](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_build/latest?definitionId=21&branchName=master)
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| [Deploy Deep Learning Model on Kubernetes](https://github.com/Microsoft/AKSDeploymentTutorialAML) | Python | Keras | Real-Time Scoring| Deploy image classification model on Kubernetes or IoT Edge for _real-time_ scoring using Azure ML | [](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_build/latest?definitionId=17&branchName=master)
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# Requirements
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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.
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