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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AzureML Development, Data Mining and Next Steps
1. .NET Development with Azure Machine Learning (AzureML)
Mark Tabladillo PhD (Microsoft MVP, SAS Expert)
Consultant SolidQ
Seattle Business Intelligence –November 24, 2014
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. Microsoft Azure ML Intro
https://www.youtube.com/watch?v=SJtNJepz-pM
https://www.youtube.com/watch?v=6IEx9G8RwP4
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. How could data science apply?
Let’s look at three companies
14. 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
17. The Power of Cloud Machine Learning
https://www.youtube.com/watch?v=z-lsheCYtug
18. 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
31. 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
33. Data Market: Sell Your Work
https://datamarket.azure.com/browse?query=machine+learning
https://datamarket.azure.com/dataset/aml_labs/anomalydetection
36. MarkTab Analysis for Gigaomhttp://research.gigaom.com/report/sector-roadmap-machine-learning-and-predictive-analytics/
37. 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
38. 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
44. 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.