41 lines
3.0 KiB
Markdown
41 lines
3.0 KiB
Markdown
# Azure Machine Learning service sample notebooks
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---
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This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK
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which allows you to build, train, deploy and manage machine learning solutions using Azure. The AML SDK
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allows you the choice of using local or cloud compute resources, while managing
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and maintaining the complete data science workflow from the cloud.
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You can find instructions on setting up notebooks [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
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You can find full documentation for Azure Machine Learning [here](https://aka.ms/aml-docs)
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## Getting Started
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These examples will provide you with an effective way to get started using AML. Once you're familiar with
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some of the capabilities, explore the repository for specific topics.
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- [Configuration](./configuration.ipynb) configures your notebook library to easily connect to an
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Azure Machine Learning workspace, and sets up your workspace to be used by many of the other examples. You should
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always run this first when setting up a notebook library on a new machine or in a new environment
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- [Train in notebook](./how-to-use-azureml/training/train-within-notebook) shows how to create a model directly in a notebook while recording
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metrics and deploy that model to a test service
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- [Train on remote](./how-to-use-azureml/training/train-on-remote-vm) takes the previous example and shows how to create the model on a cloud compute target
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- [Production deploy to AKS](./how-to-use-azureml/deployment/production-deploy-to-aks) shows how to create a production grade inferencing webservice
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## Tutorials
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The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs)
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## How to use AML
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The [How to use AML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
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- [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets.
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- [Training with Deep Learning](./how-to-use-azureml/training-with-deep-learning) - Examples demonstrating how to build deep learning models using estimators and parameter sweeps
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- [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
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- [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
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- [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
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- [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
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