Azure Machine Learning Gallery

Azure Machine Learning Gallery enables our growing community of developers and data scientists to share their machine learning pipelines, components, etc. to accelerate productivity in the machine learning lifecycle.

In this gallery, you can easily find a machine learning pipeline/component which is similar to the problem you are trying to solve, rather than starting from scratch.

Component samples

Scenario	Description
Simple Algorithm for Recommendation (SAR)	An example of how to train, score and evaluate an SAR recommender using the Azure Machine Learning component. This scenario contains the following components: Stratified Splitter: split dataset into training dataset and test dataset. SAR Training: Train a simple algorithm recommender. SAR Scoring: using test dataset to score the trained recommender. MAP: Mean Average Precision at K metric. nDCG: Normalized Discounted Cumulative Gain (nDCG) at K metric. Precision at K: Precision at K metric. Recall at K: Recall at K metric.
Spectral Residual Anomaly Detection	Anomaly detection aims to discover unexpected events or rare items in data. It is designed to be accurate, efficient and general, using Spectral Residual (SR) and Convolutional Neural Network (CNN).
Text classification using CNN	An example of how to train, and score a CNN sentiment classifier using combination of Designer built-in modules and components. This scenario contains the following components: TextCNN Train Model TextCNN Score Model TextCNN Word to Id
Image classification using AML labeling dataset	This scenario contains a component Convert Labeling Data to Image Directory to convert AML labeling dataset to ImageDirectory and other built-in modules in Designer, to build an image classification pipeline.
Natural Language Processing	There are following sample components in NLP scenario: Detect languages: Detect languages on text columns in a dataset. Semantic Textual Similarity Sequence Embedding: Model sequence data by extracting short/long term sequence features and generate emebedding in finite-dimensional space. Score Sequence Embeddings: Apply sequence embeddings to score data using transformation state from training dataset.

You can find more components here

Vote for more components

Vote for more components in the Microsoft Azure forum.

You can either vote for existing ideas or post your new idea in the Microsoft Azure Machine Learning channel. If you have specific design and script, feel free to upload in the channel. Make sure you contain component, and designer in the title if you post your new ideas.

Existing candidates:

Quick links

Pipelines - highlights of end to end machine learning workflows in multipe domains like text analytics, computer vision, recommendation, etc.
Components - a catalog of components which can be reused in different pipelines

Tutorials

Get involved

Please email us: [email protected]

Help & support

This project uses GitHub Issues to track bugs and feature requests.

Please refer to FAQ for frequently asked questions or search the existing issues before filing new issues.

Following information are useful for debugging:

Pipeline run URL
Pipeline graph
Detailed error message
70_driver_log of failed component

Contributing

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.opensource.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., status check, 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 [email protected] with any additional questions or comments.

Legal Notices

Microsoft and any contributors grant you a license to the Microsoft documentation and other content in this repository under the Creative Commons Attribution 4.0 International Public License, see the LICENSE file, and grant you a license to any code in the repository under the MIT License, see the LICENSE-CODE file.

Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries. The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks. Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653.

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Microsoft and any contributors reserve all other rights, whether under their respective copyrights, patents, or trademarks, whether by implication, estoppel or otherwise.

Containerization Preview Terms of Use

These terms of use apply only to the containerization preview.

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