1. Digital Transformation

2. OSS and Microsoft

3. Democratizing AI – Microsoft View

Agenda

Digital transformation is the next industrial revolution

Industrial Revolution 4.0

Steam, water,

mechanical

production

equipment

Division of labor,

electricity, mass

production

Electronics,

IT, automated

production

Blurring the

physical and

the digital

divide

1784 1870 1969 2016

…and it’s relevant for every industry

DIGITIZATION

Sector Overall

Assets Usage Labor

Digital spending

Digital asset stock

Trans-actions

Inter-actions

Business processes

Market making

Digital spending

on workers

Digital capital

deepeningDigitization

of work

ICT

Media

Professional services

Finance and insurance

Wholesale trade

Advanced manufacturing

Oil and gas

Utilities

Chemicals & pharmaceuticals

Basic goods manufacturing

Mining

Real estate

Transportation & warehousing

Education

Retail trade

Entertainment and recreation

Personal and local services

Government

Health care

Hospitality

Construction

Agriculture and hunting

SOURCE: McKinsey Global Institute analysis

highlow

Industry Opportunity

1

2

3

4

5

6

1 Knowledge-intensive sectors that are

highly digitized across most

dimensions

2 Capital-intensive sectors with the

potential to further digitize their

physical assets

3 Service sectors with long tail of

small firms having room to

digitize customer transactions

4 B2B sectors with the potential to

digitally engage and interact with

their customers

5 Labor-intensive sectors with the

potential to provide digital tools to

their workforce

Quasi-public and/or highly localized

sectors that lag across most

dimensions

6

Digital Masters perform better

FASHIONISTASRevenue: +6%

Profitability: -11%

Market Value: -12%

DIGITAL MASTERSRevenue: +9%

Profitability: +26%

Market Value: +12%

Revenue: -4%

Profitability: -11%

Market Value: -7%

BEGINNER

Revenue: -10%

Profitability: +9%

Market Value: +7%

CONSERVATIVE

Dig

ital C

ap

ab

ility

Leadership Capability

+9% Revenue

Creation

+12%Market

Valuation

+26%Profitability

Digital Transformation

1. Digital Transformation

2. OSS and Microsoft

3. Democratizing AI – Microsoft View

Agenda

Open Source Investments are Fueling the Momentum

SQL Server on Linux

Microsoft joins

Eclipse Foundation

HD Insight managed

service on Linux

Azure Marketplace60% of all images in Azure

Marketplace are based on

Linux/OSS

Partnership with the

Linux Foundation

for Linux on Azure

certification

600 Million+Lines of open source code

submitted to GitHub by

Microsoft engineersMicrosoft Open Source Hub

Wim Coekaerts

Oracle’s Mr. Linux

joins Microsoft

1 out of 3

1 out of 3 VMs on Azure run

Linux, and more than half of all

new VMs run Linux

Acquisition

Jenkins project

on Azure

Our Products

Our Partnerships

Our Offerings

Ross Gardler

President Apache

SW Foundation

Our Employees

Partnership

Run Linux on Windows natively

C:\Users\markhill> bash

root@localhost: #

Platinum member

Open Compute

(Project Olympus)

10+ Years of Open Source Involvement

Docker on Microsoft

Azure

O365+Moodle Integration

• Microsoft Technet: Microsoft Joins the R Consortium

• RStudio blog: Accelerating R: RStudio and the new R Consortium

• Mango Solutions press release: Mango Solutions and The R Consortium

• Oracle blog: R Consortium Launched!

• Computerworld: ESRI Joins R Consortium

Our Approach to Open Source in the Cloud

IntegrateEmbrace leading Open

Source ecosystems and

integrate Microsoft products

with agility and consistency

ReleaseRelease key Microsoft

technologies into the

Open Source domain to

build a strong ecosystem

ParticipateMicrosoft engineers to

participate in communities

and contribute to key

Open Source projects

EnableEnable Linux and Open

Source technology to be first

class citizens on Microsoft

Platforms

Open Source Partners & Ecosystem

R Server

.NET Core

Roslyn

TypeScript

F#

autorest

PowerBI Visuals

Office UI Fabric

Tools plugins

• Apache Hadoop, Spark• Hortonworks, Cloudera, MapR• Microsoft R Server• R Server for HDInsight (M/R and Spark contexts)

Parallel execution across different platforms### SETUP HADOOP ENVIRONMENT VARIABLES

myNameNode <- "master“

myUser <- "root"

myPort <- 8020

myHadoopCluster <- RxHadoopMR( sshUsername = myUser, sshHostname = myNameNode)

### HADOOP COMPUTE CONTEXT USING HDFS

rxSetComputeContext(myHadoopCluster)

### CREATE HDFS, DIRECTORY AND FILE OBJECTS

hdfsFS <- RxHdfsFileSystem(hostName=myNameNode, port=myPort)

AirlineDatabase <-file.path("EMEAIDHDemo","AirlineDemoSmall")

AirlineDataSet <- RxXdfData(file.path(AirlineDatabase,”AirlineDemoSmall.xdf”), fileSystem = hdfsFS)

### ANALYTICAL PROCESSING ###### Statistical Summary of the data: rxSummary(~ArrDelay+DayOfWeek, data= AirlineDataSet, reportProgress=1)

### CrossTab the data –rxCrossTabs(ArrDelay ~ DayOfWeek, data= AirlineDataSet, means=T)

### Linear Model and plot –hdfsXdfArrLateLinMod <- rxLinMod(ArrDelay ~ DayOfWeek + 0 , data = AirlineDataSet) plot(hdfsXdfArrLateLinMod$coefficients)

### SETUP TERADATA ENVIRONMENT VARIABLES

prodDbConn <- "Driver=Teradata;

DBCNAME=TeradataProd; Database=RevoTester;

Uid=RevoTester; pwd=######“

myTeradataSystem <-

RxInTeradata(connectionString = prodDbConn,

shareDir = "/tmp",

remoteShareDir = "/tmp/revoJobs",

revoPath = "/usr/lib64/Revo-7.0/R-3.0.2/lib64/R")

### TERADATA COMPUTE CONTEXT

rxSetComputeContext(myTeradataSystem)

### CREATE TERADATA DATA SOURCE

AirlineDemoQuery <- "SELECT * FROM

AirlineDemoSmall;"

AirlineDataSet <- RxTeradata(connectionString =

tprodDbConn, sqlQuery = AirlineDemoQuery)

### SETUP LINUX ENVIRONMENT VARIABLES

rxSetComputeContext("localpar")

### CREATE LINUX, DIRECTORY AND FILE OBJECTS

linuxFS <- rxSetFileSystem(fileSystem = RxNativeFileSystem())

AirlineDatabase <-file.path("EMEAIDHDemo","AirlineDemoSmall")AirlineDataSet <- RxXdfData(file.path(AirlineDatabase,”AirlineDemoSmall.xdf”), fileSystem = linuxFS )

1. Digital Transformation

2. OSS and Microsoft

3. Democratizing AI – Microsoft View

Agenda

인텔리전스

대시보드 및 시각화

정보 관리 빅데이터 스토어 머신러닝 및고급분석

CortanaEvent HubsHDInsight

(Hadoop and

Spark)

Stream

Analytics

Data Intelligence Action

People

Automated Systems

Apps

Web

Mobile

Bots

Bot

FrameworkSQL Data

WarehouseData Catalog

Data Lake

Analytics

Data Factory Machine

LearningData Lake Store

Cognitive

Services

Power BI

Data

Sources

Apps

Sensors

and

devices

Data

Largest R-compatible

parallel analytics and

machine learning library

Terabyte-scale machine

learning to handle 1,000x

more data

Get up to 50x faster

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Run distributed parameter

sweeps and simulations with

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Easy setup, fast results

sehan@microsoft.com