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@ -29,13 +29,20 @@ git submodule update
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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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|[Natural 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](https://github.com/microsoft/recommenders)| Examples and best practices for building recommendation systems, provided as Jupyter notebooks.|
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|[MLOps](https://github.com/microsoft/MLOps)| MLOps empowers data scientists and app developers to help bring ML models to production. |
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# Reference Architectures <a name="Reference 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/dciborow/AIArchitecturesAndPractices/tree/master/architectures/Python-ML-RealTimeServing) | 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/dciborow/AIArchitecturesAndPractices/tree/master/architectures/Python-Keras-RealTimeServing) | 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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| [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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| [Hyperparameter Tuning of Classical ML Models](https://github.com/Microsoft/MLHyperparameterTuning) | Python | CPU | Training | 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 | Scoring | 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 | Scoring | 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 | Real-Time Serving | 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 | Scoring | 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) | Python | Spark | Scoring | 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 | Training | Distributed training of ResNet50 model using Batch AI | |
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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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Dan Ciborowski