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git clone --recursive https://github.com/dciborow/AIArchitecturesAndPractices.git
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git clone --recursive https://github.com/dciborow/AIArchitecturesAndPractices.git
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```
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```
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# Architectures <a name="Architectures"></a>
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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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| Title | Language | Environment | Design | Description | Status |
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|----------------------------------------------|-------------|-------------|-------------|-----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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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 | [![Build Status](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_apis/build/status/AI%20CAT/Python-ML-RealTimeServing?branchName=master)](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_build/latest?definitionId=21&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 | [![Build Status](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_apis/build/status/AI%20CAT/Python-ML-RealTimeServing?branchName=master)](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 | [![Build Status](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_apis/build/status/AI%20CAT/Python-Keras-RealTimeServing?branchName=master)](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_build/latest?definitionId=17&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 | [![Build Status](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_apis/build/status/AI%20CAT/Python-Keras-RealTimeServing?branchName=master)](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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# Best Practices <a name="Best Practices"></a>
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| Title | Description |
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| Title | Description |
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|-------|-------------|
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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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|[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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|[Recommenders](github.com/microsoft/recommenders)| Examples and best practices for building recommendation systems, provided as Jupyter notebooks.|
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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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# Practices with Architectures <a name="Architectures"></a>
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# Best Practices with Reference Architectures <a name="Architectures"></a>
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| Title | Practice | Language | Environment | Design | Description | Status |
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| Title | Practice | Language | Environment | Design | Description | Status |
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|-------------------------------------------|----------|----------|-------------|-------------|-----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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|-------------------------------------------|----------|----------|-------------|-------------|-----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [Building a Real-time Recommendation API](https://github.com/microsoft/recommenders/blob/master/notebooks/05_operationalize/als_movie_o16n.ipynb) | Recommenders | PySpark | CPU | Real-Time Scoring| Walks through the creation of appropriate azure resources, training a recommendation model using Azure Databricks and deploying it as an API.|
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| [Building a Real-time Recommendation API](https://github.com/microsoft/recommenders/blob/master/notebooks/05_operationalize/als_movie_o16n.ipynb) | Recommenders | PySpark | CPU | Real-Time Scoring| Walks through the creation of appropriate azure resources, training a recommendation model using Azure Databricks and deploying it as an API.|
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