Being Smarter with Azure Machine Learning and R

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1 Being Smarter with Azure Machine Learning and R Leila Etaati SQL Saturday #464, Melbourne 20 th February 2016

2 Housekeeping Mobile Phones please set to stun during sessions Evaluations complete online to be in the draw for fantastic prizes SESSIONS sessions/sessionevaluation.aspx EVENT eventeval.aspx Wifi Details Login: ext-sqlsat Password: sqlsaturd4y

3 speaker Connect with the Community Event staff, volunteers and speakers are here to help and answer I attack SQL challenges by dropping onto them from above. Scan the QR code on the speaker badges to connect and network with them.

4 Who Am I? Leila Etaati Data Mining and BI Consultant Speakers in Many Microsoft SQL Server Conferences (Nzignite, SQL Rally, Code camp and SQL Saturday ) 10 Years experiences in SQL Server Co-Lead Auckland BI User Group PhD in Information System Department, Business School University of Auckland Lecturer and Tutor of BI and database

5 Agenda Introduction to Machine Learning What is Azure ML Azure ML Demos Azure ML with R Facts about Azure ML

6 What is Machin Learning/Data mining? The goal of machine learning is to build computer systems that can adapt and learn from their experience. -Tom Dietterich

7 The United States Postal Service processed over 150 billion pieces of mail in 2013 far too much for efficient human sorting. But as recently as 1997, only 10% of hand-addressed mail was successfully sorted automatically.

8 The challenge in automation is enabling computers to interpret endless variation in handwriting.

9 What is Machin Learning/Data mining? Example of Using Machine Learning

10 What is Machin Learning/Data mining? Machine Learning

11 Current State of the Business No improvement in generations Expensive Huge set-up costs of tools, expertise, and compute/storage capacity create unnecessary barriers to entry Siloed data Disconnected tools Deployment complexity Siloed and cumbersome data management restricts access to data Complex and fragmented tools limit participation in exploring data and building models Many models never achieve business value due to difficulties with deploying to production The Cloud Changes the Landscape

12 From data to decisions and actions

13 Should I used Machine Learning Predication is Small part of experiences No Past data Many Rules govern Experience Automated Predication is Core Lots of History Magic numbers in current prediction system

14 Machine Learning Concepts

15 Steps First-Understand the Business Domain Second-Understand the Business Problem Third- What is the Right Data, Right Column and Right Algorithm Last-Combine Knowledge With Machine Learning

16 Steps to Build a Machin Learning Find Potential Customers Sales More De/Refine Business Rule Find Fraud ETL Process Collect and Clean Data Split Data Test Model Performance Score Model Train Model Choose Model

17 Various Tools for Machin Learning WEKA WEKA Google Prediction API Python Machine Learning Platform R Microsoft Datamining Tools Data Mining Tools Orange Azure Machine Leaning Remote Machine Learning Tools AWS machine Lerning

18 Cortana Analytics Suite Transform data into intelligent action

19 Microsoft & Machine Learning 15 years of realizing innovation Computers work on users behalf, filtering junk Microsoft search engine built with machine learning SQL Server enables data mining Bing Maps ships with ML trafficprediction service Microsoft Kinect can watch users gestures Successful, realtime, speech-tospeech translation Microsoft launches Azure Machine Learning John Platt, Distinguished scientist at Microsoft Research Machine learning is pervasive throughout Microsoft products.

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21 Load Data From Different Location Clean data Machine Learning Algorithms R and Python Language Web services Python R studio upyter notebook

22 Fully managed Easy to use Tested solutions Deploy in minutes No software to install, no hardware to manage, and one portal to view and update Simple drag, drop and connect interface you can access and share from anywhere Access to sample experiments, tested algorithms, support for custom R, and over 350 R packages Tooled for quick deployment, hand-off and updates

23 Using past data to predict the future Churn analysis Ad targeting Image detection & classification Imagine what machine learning could do for your business. Equipment monitoring Recommendations Forecasting Spam filtering Fraud detection Anomaly detection

24 Cortana Analytics Suite successful experience with Azure ML XYU

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27 1-Which other customers have similar preferences to this one? 2-What are the most common patterns in gasoline price changes? Clustering Algorithm 1-When will this customer make another purchase? 2-How many new followers will I get next week? Regression Algorithm 1-Will this customer click on the top link? 2-Which offer should this customer receive? Classification Algorithm

28 Predictive Maintenance Two-class classification Multi-class classification Regression Unsupervise d learning Should I replace this part now? When will this part fail? Will this tire fail in the next thousand miles? What is the remaining useful life of this aircraft engine? Which vehicle needs servicing most urgently? Which groups of sensors in this jet engine tend to vary with (and against) each other?

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30 Demo On Azure ML

31 Titanic Classification Algorithm Survived or Not Titanic sank on15 April 1912 after colliding with an iceberg

32 What is? The R statistical programming language is a free open source package based on the S language developed by Bell Labs. The language is very powerful for writing programs. Many statistical functions are already built in. Contributed packages expand the functionality to cutting edge research. Since it is a programming language, generating computer code to complete tasks is required. R and Data Mining : Examples and Case Studies by Yanchang Zhao

33 Growth In Demand? Rexer Data Mining survey R is the highest paid IT skill Dice.come, Jan 2014 R most used-data science language after SQL-O Reilly, Jan 2014 R is used by 70% of data miners. Rexer, Sept 2013 R is #15 all programming languages. REdMonk, Jan 2014 R growing faster than any other data science language. KDNuggs R is in-memory and limited in size of data that you can process.

34 Demo on R

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36 Features Usability Cost Support End to end Product Canned algorithm Not possible to change algorithm DMX Code More Visual Excel Add on It s not easy to start All users can use If you purchase SQL Sever: Free Few books and small online community Current and up to date algorithm Integration with R and Python Cloud base REST format Hard to interpret Drag and Drop UI Customize the Algorithm Excel Add on Free version, limited options More online Community

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38 Questions? Please make sure you visit our fantastic sponsors:

39 How did we do? Please complete an online Evaluation to be included the draw for a fantastic prize! There is a prize for each session timeslot and for the overall event survey so the more feedback you provide the more chances you have. Session Surveys Post-Event Survey sessions/sessionevaluation.aspx eventeval.aspx