Hottest Buzz Out There: Integrating Predictive Analytics, SharePoint and Azure Machine Learning

Fernando Leitzelar, PMP Vice President ITSM

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SpeakerFernando Leitzelar, PMP

Vice President ITSM

Fernando Leitzelar is a senior SharePoint Evangelist and Vice-president with a Large Bank As a consultant he regularly interfaced with clients and development teams to design SharePoint-based solutions. Fernando has progressively held SharePoint positions ranging from developer and administrator to Architect and Manager. He has been a SharePoint Saturday Speaker since 2010, having worked extensively on designing and architecting sophisticated SharePoint based applications. He maintains expertise in Office 365, Azure, SharePoint 2016/2013/2010/2007/2003, BI and Machine Learning Solutions.

Twitter: @fleitzelar

Blog: http://sharepointusa.wordpress.com

• What and How ?Introduction to ML and

Predictive Analytics

• Predictive Analytics using Machine LearningPredictive Analytics

• Machine Learning Studio• Building ML models• Create a Web Service

Azure Machine Learning

• Consume Machine Learning ModelSharePoint Online

Agenda

Predictive AnalyticsPredicting future performance based on historical data

Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.

ADVANCED ANALYTICSBEYOND BUSINESS INTELLIGENCE

From Descriptive to Prescriptive

Analytics Maturity Level

What happened ?• Reporting:

Statistics

Why did it happen ?• Analysis: Excel, OLAP

What is happening ?• Monitoring: Dashboards,

Scorecards

What will happen ?• Prediction: Data Mining,

Machine Learning

Evolution of Predictive Analytics

2000s

1990s

1980s

2010s

Machine LearningComputer Systems that improve with experience

CLASSES OF LEARNING PROBLEMS• Classification: Assign a category to each item (Chinese | French | Indian | Italian |

Japanese restaurant).

• Regression: Predict a real value for each item (stock/currency value, temperature).

• Ranking: Order items according to some criterion (web search results relevant to a user query).

• Clustering: Partition items into homogeneous groups (clustering twitter posts by topic).

• Dimensionality reduction: Transform an initial representation of items into a lower-dimensional representation while preserving some properties (preprocessing of digital images).

WHAT IS MACHINE LEARNING?

Methods and Systems that …

Adapt based on recorded

data

Predict new data based on recorded

data

Optimize an action given

a utility function

Extracthidden

structure from the

data

Summarizedata into concise

descriptions

MACHINE LEARNING IS NOT

Methods and Systems that …

can yield Garbage-In-Knowledge-

Out

perform good predictions without data modeling &

feature engineering

Silver-bullet for all data-

driven tasks –it’s a powerful

data tool!

are a replacement for business rules – they

augment them!

TRANSFORMATIONAL

A Good Machine Learning Tool would allows us to

solve extremely hard problems betterextract more value from Big Data

approach human intelligence

drive a shift in business analytics

Data Science is far too complex today• Access to quality ML algorithms, cost is high.• Must learn multiple tools to go end2end,

from data acquisition, cleaning and prep,machine learning, and experimentation.

• Ability to put a model into production.

This must get simpler, it simply won’t scale!

PROBLEMS ML NEEDS TO ADDRESS …

PREDICTIVE ANALYTICS AND ML SCENARIOS

Predictive maintenance

Classification

Regression

Clustering

Anomaly Detection

AZURE ML ALGORITHMS

ADVANCED ANALYTICS TODAYHARD-TO-REACH SOLUTIONS

Break away from industry limitations

WHAT HAS IT GOT TO DO WITH SHAREPOINT ?

MLStudio

API

M

AZURE MACHINE LEARNING

AZURE MACHINE LEARNING

Azure Portal

ML Studio

ML API Service

Operational Team

Data Scientists and Data Professionals

Software Developers

AZURE ML STUDIO

MICROSOFT AZURE MACHINE LEARNINGBuilt for a cloud-first, mobile-first world

Step 1

• Data Preparation and Feature Engineering

Step 2

• Train and Evaluate Model

Step 3

• Deploy Web Service

BUILDING ML MODEL

RECEIVER OPERATING CHARACTERISTIC CURVE

ROC CURVE

ROC CURVE

Reduce complexity to broaden participationMICROSOFT AZURE MACHINE LEARNING

FEATURES AND BENEFITS

• Accessible through a web browser, no software to install;

• Collaborative work with anyone, anywhere via Azure workspace

• Visual composition with end2end support for data science workflow;

• Best in class ML algorithms;• Extensible, support for R.

MICROSOFT AZURE MACHINE LEARNING

FEATURES AND BENEFITSRapid experimentation to create a better modelImmutable library of models, search discover and reuse;Rapidly try a range of features, ML algorithms and modeling strategies;Quickly deploy model as Azure web service to our ML API service.

• https://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-choice/

• https://azure.microsoft.com/en-us/documentation/services/machine-learning/

• Azure Machine Learning Essentials Book

• https://channel9.msdn.com/blogs/Cloud-and-Enterprise-Premium/Building-Predictive-Maintenance-Solutions-with-Azure-Machine-Learning

• Channel 9

AZURE MACHINE LEARNING RESOURCES

KEY CONCEPTSData

Model

Parameters

Learning Prediction

Decision Making

Utility Function

STEPS TO BUILD A MACHINE LEARNING SOLUTION

AZURE DATA MARKET ML APPLICATIONS

• http://text-analytics-demo.azurewebsites.net/

• https://churn.cloudapp.net

• http://how-old.net/#