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MSBIP møde nr. 25 - Azure ML
This document provides an overview of Azure Machine Learning including an introduction to the service, differences between Azure ML and SSAS Data Mining, demos of building and consuming ML models, and a quick introduction to other relevant Azure tools like Azure Stream Analytics, Azure Data Factory, and Azure Intelligent Systems Service. The presenter has experience with SQL Server BI, .NET, and is a BI developer but not a data scientist.
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MSBIP møde nr. 25 - Azure ML
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- 2. Agenda About me Introduction to Azure Machine Learning Differences between Azure ML and SSAS Data Mining Demo • Azure ML • Consume ML Webservice from Power Query Quick intro to other relevant tools in Azure
- 3. About me DavidBojsen dlb@kapacity.dk 36 years old, married, 2 children (7 and 17) 8 years of experience with SQL Server and Microsoft BI 11 years of experience with C# and .NET BI Manager at TELMORE since April 2009 Senior Business Analytics Architect at Kapacity since April 2013 BI Developer – NOT data scientist
- 4. Introduction to AzureMachine Learning Azure Cloud Service Prebuilt standard models for data discovery and prediction (unsupervised learning or supervised learning) Framework for building new and/or selling models Allow you to build a model using R or/and standard algorithms About finding an algorithm/model for determining an outcome (well-defined problem – e.g. given a set of features what is the probability of x, what is the predicted price of selling a house, what classification does an ”entity” belong to) Trained AND scored based on a dataset Inputs is a predefined set of features that can be used to predict an outcome
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- 6. Azure ML vs.SSAS Data Mining Azure ML Pay as you Go Fixed set of algorithms extensible by using R Easy visual interface Cloud (first) Batch / online requests SSAS Data Mining Free (With SQL Server) Fixed set of algoritms (somewhat outdated) Integrated in Visual Studio On-premise Batch requests Good for data discovery (thru Excel add-in)
- 7. Extensability R •Access to over 400 of the most popular CRAN packages • Your own custom code Python • Python Tools for Visual Studio (Check Azure ML Blog) • Not yet integrated in ML Studio
- 8. DEMO Building anexperiment to test/train/select a model Building an experiment to publish a model Testing model manually Consuming a model from Power Query
- 9. Other relevant servicesin Azure Azure Stream Analytics • Think Stream Insight In The Cloud Azure Data Factory • Think Integration Services In The Cloud (for cloud and on-prem) Azure Service Bus Azure Event Hubs Azure Intelligent Systems Service • All related to Internet Of Things
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- 12. Internet Of Things Data ”Things” ”Devices” ISS Agent
- 13. Want to knowmore? • Azure Machine Learning • Service - http://azure.com/ML • Documentation – http://aka.ms/MLDS • Pricing – http://aka.ms/MLPricing • Studio – http://studio.azureml.com • FAQ – http://aka.ms/MLFAQ • MarketPlace – http://aka.ms/MLMarket • Blog – http://aka.ms/MLBlog • Azure Stream Analytics – http://azure.microsoft.com/en-us/services/stream-analytics/ • Azure Data Factory – http://azure.microsoft.com/en-us/services/data-factory/ • http://www.internetofyourthings.com/