Insider's introduction to microsoft azure machine learning: 201411 Seattle Business Intelligence
23
Insider's Introduction to Microsoft Azure Machine Learning (AzureML) Mark Tabladillo PhD (Microsoft MVP, SAS Expert) Consultant SolidQ Seattle Business Intelligence – November 5, 2014
-
Upload
mark-tabladillo -
Category
Data & Analytics
-
view
909 -
download
5
description
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
Transcript of Insider's introduction to microsoft azure machine learning: 201411 Seattle Business Intelligence
Insider's Introduction to Microsoft Azure Machine Learning (AzureML)Mark Tabladillo PhD (Microsoft MVP, SAS Expert)
Consultant SolidQ
Seattle Business Intelligence – November 5, 2014
Mark TabSQL Server MVP; SAS Expert
Consulting
Training
Teaching
Presenting
Linked In
@MarkTabNet
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 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 mining apply?
Let’s look at three companies
Telecommunications
Oil and Gas
Volkswagen Group
What Why How
Relational Data Warehouse
Data integrity, structure, fast, well-known, governance, fixed schemas
ETL, BIML, Index
Hadoop & HDInsightUnstructured data, large volumes of text, flexible schemas
Hbase, Map Reduce, HDFS
Tabular Fast analytics, agility, preserves types In-memory
MultidimensionalOLAP
Fast analytics, large data volumes Preaggregated calculations
Data Mining & Machine Learning
Complex analytics, discovery, predictive models, forecasting
Estimations
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
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
People
MarkTab Analysis for Gigaom
http://research.gigaom.com/report/sector-roadmap-machine-learning-and-predictive-analytics/
Free Tier: AzureML
Free Tier: AzureML
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
AbstractMicrosoft 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
