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Getting Started
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First try the example 01.run-experiment to connect to your workspace and run a basic experiment using Azure Machine Learning Python SDK, and then 02.deploy-web-service to deploy a model as a web service.

Then move to more comprehensive examples in tutorials folder.

See also:

Important: You must select Python 3.6 as the kernel for your notebooks to use the SDK.

Note: The config.json file in this folder was created for you with details of your Azure Machine Learning service workspace. Both these notebooks use this file to connect to your workspace. You can also copy this file into other places where you have code that needs this connection.

Modified on: Feb 4, 2019
AnomalyDetector
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The Anomaly Detector API lets you monitor and detect abnormalities in your time series data without previous experience in machine learning. The API adapts by automatically identifying and applying the best fitting statistical models to your data, regardless of industry, scenario, or data volume. These python notebooks cover the following examples.

Use previously seen data points to determine if the latest one in the data set is an anomaly. This example simulates using the Anomaly Detector API on streaming data by iterating over the data set and sending API requests at predetermined positions. By calling the API with each new data point you generate, you can monitor your data as it's created.

Use a time series data set to detect any anomalies that might exist as a batch. This example sends example data sets in a single Anomaly Detector API request. |

These python notebooks show you how to start detecting anomalies in your data with the Anomaly Detector API, and visualizing the information returned by it.

A Cognitive Services API account with access to the Anomaly Detector API. If you don't have an Azure subscription, you can create an account for free. You can get your subscription key and API endpoint from the Azure portal after creating your account, or Azure website after activating a free trial.

  1. Sign in, and click Clone, in the upper right corner.
  2. Click Run on free compute
  3. Select one of the notebooks for this sample, start with "Batch anomaly detection with Anomaly Detector API.ipynb"
  4. Add your valid Anomaly Detector API subscription key to the variable.
  5. You may need to update the too if you created resource from other Azure regions. If you selected West US 2 when creating the resource, no need to change here.
  6. On the top menu bar, click Cell, then Run All.

For more information, see the Anomaly Detector API documentation.

Modified on: Mar 26, 2019

For full documentation for Azure Machine Learning service, visit https://aka.ms/aml-docs.

To run the notebooks in this repository use one of these methods:

  1. Import sample notebooks into Azure Notebooks.

  2. Follow the instructions in the 00.configuration notebook to create and connect to a workspace.

  3. Open one of the sample notebooks.

    Make sure the Azure Notebook kernel is set to when you open a notebook.

Video walkthrough:

  1. Setup a Jupyter Notebook server and install the Azure Machine Learning SDK.

  2. Clone this repository.

  3. You may need to install other packages for specific notebook.

    • For example, to run the Azure Machine Learning Data Prep notebooks, install the extra dataprep SDK:
  4. Start your notebook server.

  5. Follow the instructions in the 00.configuration notebook to create and connect to a workspace.

  6. Open one of the sample notebooks.

Note: Looking for automated machine learning samples? For your convenience, you can use an installation script instead of the steps below for the automated ML notebooks. Go to the automl folder README and follow the instructions. The script installs all packages needed for notebooks in that folder.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Modified on: Nov 16, 2018
AzureML
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For full documentation for Azure Machine Learning service, visit https://aka.ms/aml-docs.

To run the notebooks in this repository use one of these methods:

  1. Import sample notebooks into Azure Notebooks.

  2. Follow the instructions in the 00.configuration notebook to create and connect to a workspace.

  3. Open one of the sample notebooks.

    Make sure the Azure Notebook kernel is set to when you open a notebook.

Video walkthrough:

  1. Setup a Jupyter Notebook server and install the Azure Machine Learning SDK.

  2. Clone this repository.

  3. You may need to install other packages for specific notebook.

    • For example, to run the Azure Machine Learning Data Prep notebooks, install the extra dataprep SDK:
  4. Start your notebook server.

  5. Follow the instructions in the 00.configuration notebook to create and connect to a workspace.

  6. Open one of the sample notebooks.

Note: Looking for automated machine learning samples? For your convenience, you can use an installation script instead of the steps below for the automated ML notebooks. Go to the automl folder README and follow the instructions. The script installs all packages needed for notebooks in that folder.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Modified on: Dec 27, 2018
gebaml
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Modified on: Jan 5, 2019
gebatest
0 clones
Modified on: Jul 18, 2018
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