adding AML references
adding AML references, as PG was asking which ones used AML
@@ -20,9 +20,9 @@ git clone --recursive https://github.com/Microsoft/AIReferenceArchitectures.git
# Tutorials <a name="tutorials"></a>
| Tutorial | Environment | Description | Status |
|----------------------------------------------|-------------|-----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [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 | [![Build Status](https://dev.azure.com/customai/AKSDeploymentTutorialAML/_apis/build/status/Microsoft.AKSDeploymentTutorialAML?branchName=master)](https://dev.azure.com/customai/AKSDeploymentTutorialAML/_build/latest?definitionId=11&branchName=master) |
-| [Deploy Classic ML Model on Kubernetes](https://github.com/Microsoft/MLAKSDeployAML) | Python CPU | Train LightGBM model locally using Azure Machine Learning, deploy on Kubernetes or IoT Edge for _real-time_ scoring | ![](https://dev.azure.com/customai/MLAKSDeployAMLPipeline/_apis/build/status/Microsoft.MLAKSDeployAML?branchName=master) |
-| [Hyperparameter Tuning of Classical ML Models](https://github.com/Microsoft/MLHyperparameterTuning) | Python CPU | Train LightGBM model locally and run Hyperparameter tuning using Hyperdrive | ![](https://dev.azure.com/customai/MLHyperparameterTuningPipeline/_apis/build/status/Microsoft.MLHyperparameterTuning?branchName=master) |
+| [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 | [![Build Status](https://dev.azure.com/customai/AKSDeploymentTutorialAML/_apis/build/status/Microsoft.AKSDeploymentTutorialAML?branchName=master)](https://dev.azure.com/customai/AKSDeploymentTutorialAML/_build/latest?definitionId=11&branchName=master) |
+| [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 | ![](https://dev.azure.com/customai/MLAKSDeployAMLPipeline/_apis/build/status/Microsoft.MLAKSDeployAML?branchName=master) |
+| [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 | ![](https://dev.azure.com/customai/MLHyperparameterTuningPipeline/_apis/build/status/Microsoft.MLHyperparameterTuning?branchName=master) |
| [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 | [![Build Status](https://dev.azure.com/customai/BatchScoringDeepLearningModelsWithAMLPipeline/_apis/build/status/Azure.Batch-Scoring-Deep-Learning-Models-With-AML?branchName=master)](https://dev.azure.com/customai/BatchScoringDeepLearningModelsWithAMLPipeline/_build/latest?definitionId=9&branchName=master) |
| [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 | ![](https://dev.azure.com/customai/AMLBatchScoringPipeline/_apis/build/status/Microsoft.AMLBatchScoringPipeline?branchName=master) |
| [Deploy R ML Model on Kubernetes](https://github.com/Azure/RealtimeRDeployment) | R CPU | Deploy ML model for _real-time_ scoring on Kubernetes | |
