.NET Development with Azure Machine Learning (AzureML)Mark Tabladillo PhD (Microsoft MVP, SAS Expert)
Consultant SolidQ
Seattle Business Intelligence – November 24, 2014
Entertainment: Pacman 1981 in 2014https://www.youtube.com/watch?v=flfE-cX8qjM
Meet your neighbors
Mark TabSQL Server MVP; SAS Expert
Consulting
Training
Teaching
Presenting
Linked In
@MarkTabNet
What is Azure ML?
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
Microsoft Azure ML Introhttps://www.youtube.com/watch?v=SJtNJepz-pM
https://www.youtube.com/watch?v=6IEx9G8RwP4
Microsoft Azure Machine LearningMicrosoft 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.
How could data science apply?
Let’s look at three companies
Telecommunications
Oil and Gas
Volkswagen Group
What Why How
Relational Data Warehouse
Store data in table; query faster; handles lots of transactions
Dimensional models; optimized reads; indexing
Hadoop & HDInsightStore large amounts of data; unstructured data, flexible schemas
Distributed computing; virtualization
Tabular Fast ad-hoc, flexible In-memory
MultidimensionalOLAP
Aggregations Store aggregations; semantic model
Data Mining & Machine Learning
Predictions, descriptions, prescriptions Estimations; Query the model
Demos: Technical Overview of AzureMLEmpirical Technical Description
The Power of Cloud Machine Learninghttps://www.youtube.com/watch?v=z-lsheCYtug
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 dataframe and 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
Bloghttp://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
Applications Development
SQL Server Data Mining: Analysis Serviceshttp://sqlserverdatamining.com
Data mining add-in for business analysts
• Ease of use
• Rich data mining
• Scalable
Split Personality of SSAS
SS
SQL
AS
NoSQL
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*
What Enterprise Tools support SSAS?
Data Mining
SSMS SSIS PowerShell
SSAS Logical Architecture
SSAS Physical Architecture
Project Sampleshttp://sqlserverdatamining.com
Path for Next Steps
People
Difference in Proportions Test
Lexicon Based Sentiment Analysis
Forecasting-Exponential Smoothing
Forecasting - ETS+STL
Forecasting-AutoRegressive Integrated
Moving Average (ARIMA)
Normal Distribution Quantile Calculator
Normal Distribution Probability Calculator
Normal Distribution Generator
Binomial Distribution Probability Calculator
Binomial Distribution Quantile Calculator
Binomial Distribution Generator
Multivariate Linear Regression
Survival Analysis
Binary Classifier
Cluster Model
datamarket.azure.com
Codeplex Project for AzureMLhttp://azuremlexcel.codeplex.com/
Data Market: Sell Your Workhttps://datamarket.azure.com/browse?query=machine+learning
https://datamarket.azure.com/dataset/aml_labs/anomalydetection
Free Tier: AzureML
Free Tier: AzureML
MarkTab Analysis for Gigaom
http://research.gigaom.com/report/sector-roadmap-machine-learning-and-predictive-analytics/
SoftwareDreamspark (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 -- MarkTabhttp://blogs.msdn.com/b/mvpawardprogram/archive/2014/08/04/primer-on-power-bi-business-intelligence.aspx
ResourcesMachine 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
Organizations
Professional Association for SQL Server http://www.sqlpass.org
PASS Business Analytics Conference http://www.passbaconference.com
PASS Data Science (virtual chapter)
DataGo Get It
AbstractAzure 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.