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.Net development with Azure Machine Learning (AzureML) Nov 2014

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Azure Machine Learning provides enterprise-class machine learning and data mining to the cloud. This presenter will cover 1) what AzureML is, 2) technical overview of AzureML for application development, 3) a reminder to consider SQL Server Data Mining, and 4) a recommend path for resources and next steps.

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.Net development with Azure Machine Learning (AzureML) Nov 2014

  1. 1. .NET Development with Azure Machine Learning (AzureML) Mark Tabladillo PhD (Microsoft MVP, SAS Expert) Consultant SolidQ Seattle Business Intelligence –November 24, 2014
  2. 2. Entertainment: Pacman1981 in 2014 https://www.youtube.com/watch?v=flfE-cX8qjM
  3. 3. Meet your neighbors
  4. 4. Mark Tab SQL Server MVP; SAS Expert Consulting Training Teaching Presenting Linked In @MarkTabNet
  5. 5. What is Azure ML?
  6. 6. 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
  7. 7. Microsoft Azure ML Intro https://www.youtube.com/watch?v=SJtNJepz-pM https://www.youtube.com/watch?v=6IEx9G8RwP4
  8. 8. 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.
  9. 9. How could data science apply? Let’s look at three companies
  10. 10. Telecommunications
  11. 11. Oil and Gas
  12. 12. Volkswagen Group
  13. 13. What Why How Relational Data Warehouse Store data in table; query faster; handles lots of transactions Dimensionalmodels; optimized reads; indexing Hadoop & HDInsight Storelarge amounts of data; unstructured data, flexible schemas Distributedcomputing; virtualization Tabular Fastad-hoc, flexible In-memory MultidimensionalOLAP Aggregations Storeaggregations; semanticmodel Data Mining & Machine Learning Predictions, descriptions,prescriptions Estimations; Query the model
  14. 14. Demos: Technical Overview of AzureML Empirical Technical Description
  15. 15. The Power of Cloud Machine Learning https://www.youtube.com/watch?v=z-lsheCYtug
  16. 16. 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
  17. 17. Blog http://blogs.technet.com/b/francesco_diaz/archive/2014/08/30/using-language-r- and-azure-machine-learning-to-load-data-from-azure-sql-database.aspx
  18. 18. Applications Development
  19. 19. SQL Server Data Mining: Analysis Services http://sqlserverdatamining.com
  20. 20. Data mining add-in for business analysts •Ease of use •Rich data mining •Scalable
  21. 21. Split Personality of SSAS SS SQL AS NoSQL
  22. 22. Data platform: SQL Server 2014 Database Services SQL Server* SQL Azure* ReplicationSQL Azure Data Sync* Full Text & Semantic Search* Data Integration Services Integration Services* Master Data Services* Data Quality Services* StreamInsight* Project “Austin”* Analytical Services Analysis Services* Data Mining PowerPivot* Reporting Services Reporting Services* SQL Azure Reporting* Report Builder Power View*
  23. 23. What Enterprise Tools support SSAS? Data Mining SSMS SSIS PowerShell
  24. 24. SSAS Logical Architecture
  25. 25. SSAS Physical Architecture
  26. 26. Project Samples http://sqlserverdatamining.com
  27. 27. Path for Next Steps
  28. 28. People
  29. 29. 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
  30. 30. Codeplex Project for AzureML http://azuremlexcel.codeplex.com/
  31. 31. Data Market: Sell Your Work https://datamarket.azure.com/browse?query=machine+learning https://datamarket.azure.com/dataset/aml_labs/anomalydetection
  32. 32. Free Tier: AzureML
  33. 33. Free Tier: AzureML
  34. 34. MarkTab Analysis for Gigaomhttp://research.gigaom.com/report/sector-roadmap-machine-learning-and-predictive-analytics/
  35. 35. Software Dreamspark(students); BizSpark(businesses) SQL Server 2014 Enterprise (includes database engine, Analysis Services, SSMS and SSDT) http://www.microsoft.com/en-us/server-cloud/products/sql-server/default.aspx Microsoft Office http://office.microsoft.com/en-us/ Primer on Power BI --MarkTab http://blogs.msdn.com/b/mvpawardprogram/archive/2014/08/04/primer-on-power-bi-business- intelligence.aspx
  36. 36. 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
  37. 37. Organizations Professional Association for SQL Server http://www.sqlpass.org PASS Business Analytics Conference http://www.passbaconference.com
  38. 38. PASS Data Science (virtual chapter)
  39. 39. Data Go Get It
  40. 40. Abstract Azure Machine Learning provides enterprise-class machine learning and data mining to the cloud. This presenter will cover 1) what AzureML is, 2) technical overview of AzureML for application development, 3) a reminder to consider SQL Server Data Mining, and 4) a recommend path for resources and next steps.

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