Insider's introduction to microsoft azure machine learning: 201411 Seattle Business Intelligence
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Microsoft has introduced a new technology for developing analytics applications in the cloud. The presenter has an insider's perspective, having actively provided feedback to the Microsoft team which has been developing this technology over the past 2 years. This session will 1) provide an introduction to the Azure technology including licensing, 2) provide demos of using R version 3 with AzureML, and 3) provide best practices for developing applications with Azure Machine Learning
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Insider's introduction to microsoft azure machine learning: 201411 Seattle Business Intelligence
- 1. Insider's Introduction to Microsoft Azure Machine Learning (AzureML) Mark Tabladillo PhD (Microsoft MVP, SAS Expert) Consultant SolidQ Seattle Business Intelligence –November 5, 2014
- 2. Mark Tab SQL Server MVP; SAS Expert Consulting Training Teaching Presenting Linked In @MarkTabNet
- 3. Machine Learning / Predictive Analytics Vision Analytics Recommenda-tion engines Advertising analysis Weather forecasting for business planning Social network analysis Legal discovery and document archiving Pricing analysis Fraud detection Churn analysis Equipment monitoring Location-based tracking and services Personalized Insurance Machine learning & predictive analytics are core capabilities that are needed throughout your business
- 4. Microsoft Azure Machine Learning Microsoft Azure Machine Learning, a fully-managed cloud service for building predictive analytics solutions, helps overcome the challenges most businesses have in deploying and using machine learning. How? By delivering a comprehensive machine learning service that has all the benefits of the cloud. Azure Ml brings together the capabilities of new analytics tools, powerful algorithms developed for Microsoft products like Xbox and Bing, and years of machine learning experience into one simple and easy-to-use cloud service.
- 5. How could data mining apply? Let’s look at three companies
- 7. Oil and Gas
- 10. What Why How Relational Data Warehouse Data integrity, structure,fast, well-known, governance, fixed schemas ETL, BIML,Index Hadoop & HDInsight Unstructureddata, large volumes of text, flexible schemas Hbase, Map Reduce,HDFS Tabular Fast analytics,agility, preserves types In-memory MultidimensionalOLAP Fast analytics, largedata volumes Preaggregatedcalculations Data Mining & Machine Learning Complexanalytics, discovery, predictive models, forecasting Estimations
- 12. Integration with R •Data scientists can bring their existing assets in R and integrate them seamlessly into their Azure ML workflows. •Using Azure ML Studio, R scripts can be operationalized as scalable, low latency web services on Azure in a matter of minutes! •Data scientists have access to over 400 of the most popular CRAN packages, pre-installed. Additionally, they have access to optimized linear algebra kernels that are part of the Intel Math Kernel Library. •Data scientists can visualize their data using R plotting libraries such as ggplot2. •The platform and runtime environment automatically recognize and provide extensibility via high fidelity bi-directional dataframeand schema bridges, for interoperability. •Developers can access common ML algorithms from R and compose them with other algorithms provided by the Azure ML platform. http://blogs.technet.com/b/machinelearning/archive/2014/09/17/ extensibility-and-r-support-in-the-azure-ml-platform.aspx
- 15. Difference in Proportions Test Lexicon Based Sentiment Analysis Forecasting-Exponential Smoothing Forecasting -ETS+STL Forecasting-AutoRegressiveIntegrated Moving Average (ARIMA) Normal Distribution QuantileCalculator Normal Distribution Probability Calculator Normal Distribution Generator Binomial Distribution Probability Calculator Binomial Distribution QuantileCalculator Binomial Distribution Generator Multivariate Linear Regression Survival Analysis Binary Classifier Cluster Modeldatamarket.azure.com
- 16. People
- 17. MarkTab Analysis for Gigaomhttp://research.gigaom.com/report/sector-roadmap-machine-learning-and-predictive-analytics/
- 20. Resources Machine Learning Blog http://blogs.technet.com/b/machinelearning/ Forum http://social.msdn.microsoft.com/forums/azure/en- US/home?forum=MachineLearning SQL Server Data Mining http://sqlserverdatamining.com MarkTab http://marktab.net
- 23. Abstract Microsoft has introduced a new technology for developing analytics applications in the cloud. The presenter has an insider's perspective, having actively provided feedback to the Microsoft team which has been developing this technology over the past 2 years. This session will 1) provide an introduction to the Azure technology including licensing, 2) provide demos of using R version 3 with AzureML, and 3) provide best practices for developing applications with Azure Machine Learning