net development with azure machine learning (azureml) nov 2014

.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.