Copyright (c) Microsoft Corporation
Licensed under the MIT License

Overview

Traditional machine learning model development is resource-intensive, requiring significant domain/statistical knowledge and time to produce and compare dozens of models. With automated machine learning, the time it takes to get production-ready ML models with great ease and efficiency highly accelerates. However, the Automated Machine Learning does not yet provide much in terms of data preparation and feature engineering. The AcceleratedML framework tries to solve this problem at scale as well as simplifies the overall process for the user. It leverages the Azure Automated ML coupled with components like Data Profiler, Data Sampler, Data Cleanser, Anomaly Detector which ensures quality data as a critical pre-step for building the ML model. This is powered with Telemetry, DevOps and Power BI integration, thus providing the users with a one-stop shop solution to productionize any ML model. The framework aims at ‘Democratizing’ AI all the while maintaining the vision of ‘Responsible’ AI.

Getting Started

Prerequisites

1. Azure Databricks
2. Auto Tune Model Notebooks (Master, Trigger notebooks)
3. Azure ML Services workspace
4. Python cluster in Databricks with configurations as mentioned in Installations link above (PyPi library azureml-sdk[automl],azureml-opendatasets, azureml-widgets in cluster)
5. For sample to be used in notebook refer- Real Estate Data )

How to use it

1. AMLMasterNotebook: Contains all the base functions used Data Acquisition, EDA, Sampling, Cleansing, Anomaly Detection, Azure AutoML Trigger, AutoML Trigger bypassing authentication to Azure ML(used for pipelining the notebook).
2. AMLMasterNotebook_Trigger: Function calls in order to perform a pipeline of tasks.

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.

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.

Note: To know in detail of the workings of Rules Engine, please visit Accelerated ML WiKi.