1. The document discusses digital transformation and how it is relevant across industries. It notes that digital masters who are leaders in digital capabilities outperform others in revenue, profitability, and market value. 2. It then discusses Microsoft's increasing involvement in open source software, noting contributions to projects like Linux and partnerships with organizations like the Linux Foundation. Over 600 million lines of code have been submitted to GitHub by Microsoft engineers. 3. The last section discusses Microsoft's vision for democratizing AI and making advanced analytics and machine learning more accessible through platforms like Azure that integrate services from Cognitive Services, HDInsight, and other tools.

2. 1. Digital Transformation
2. OSS and Microsoft
3. Democratizing AI – Microsoft View
Agenda

3. 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

4. …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
deepening
Digitization
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

5. Digital Masters perform better
FASHIONISTAS
Revenue: +6%
Profitability: -11%
Market Value: -12%
DIGITAL MASTERS
Revenue: +9%
Profitability: +26%
Market Value: +12%
Revenue: -4%
Profitability: -11%
Market Value: -7%
BEGINNER
Revenue: -10%
Profitability: +9%
Market Value: +7%
CONSERVATIVE
DigitalCapability
Leadership Capability
+9%
Revenue
Creation
+12%
Market
Valuation
+26%
Profitability

7. Digital Transformation

8. 1. Digital Transformation
2. OSS and Microsoft
3. Democratizing AI – Microsoft View
Agenda

9. Open Source Investments are Fueling the Momentum
SQL Server on Linux
Microsoft joins
Eclipse Foundation
HD Insight managed
service on Linux
Azure Marketplace
60% 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 engineersMicrosoftOpenSourceHub
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:Usersmarkhill> bash
root@localhost: #
Platinum member
Open Compute
(Project Olympus)

10. 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

11. Our Approach to Open Source in the Cloud
Integrate
Embrace leading Open
Source ecosystems and
integrate Microsoft products
with agility and consistency
Release
Release key Microsoft
technologies into the
Open Source domain to
build a strong ecosystem
Participate
Microsoft engineers to
participate in communities
and contribute to key
Open Source projects
Enable
Enable 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

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

13. Parallel execution across different platforms
### SETUP HADOOP ENVIRONMENT VARIABLES
myNameNode <- "master“
myUser <- "root"
myPort <- 8020
myHadoopCluster <- RxHadoopMR( sshUserna
me = myUser, sshHostname = myNameNode)
### HADOOP COMPUTE CONTEXT USING HDFS
rxSetComputeContext(myHadoopCluster)
### CREATE HDFS, DIRECTORY AND FILE OBJECTS
hdfsFS <- RxHdfsFileSystem(hostName=myNam
eNode, port=myPort)
AirlineDatabase <-file.path("EMEAIDHDemo","Air
lineDemoSmall")
AirlineDataSet <- RxXdfData(file.path(AirlineData
base,”AirlineDemoSmall.xdf”), fileSystem = hdfsF
S)
### 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 VAR
IABLES
rxSetComputeContext("localpar")
### CREATE LINUX, DIRECTORY AND F
ILE OBJECTS
linuxFS <- rxSetFileSystem(fileSyst
em = RxNativeFileSystem())
AirlineDatabase <-file.path("EMEAI
DHDemo","AirlineDemoSmall")
AirlineDataSet <- RxXdfData(file.p
ath(AirlineDatabase,”AirlineDemoS
mall.xdf”), fileSystem = linuxFS )

14. 1. Digital Transformation
2. OSS and Microsoft
3. Democratizing AI – Microsoft View
Agenda

27. 인텔리전스
대시보드 및 시각화
정보 관리 빅데이터 스토어 머신러닝 및
고급분석
CortanaEvent Hubs
HDInsight
(Hadoop and
Spark)
Stream
Analytics
Data Intelligence Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Bot
Framework
SQL Data
WarehouseData Catalog
Data Lake
Analytics
Data Factory
Machine
Learning
Data Lake Store
Cognitive
Services
Power BI
Data
Sources
Apps
Sensors
and
devices
Data

28. Largest R-compatible
parallel analytics and
machine learning library
Terabyte-scale machine
learning to handle 1,000x
more data
Get up to 50x faster
performance
Run distributed parameter
sweeps and simulations with
existing R functions
Enterprise-grade security
and support
Easy setup, fast results

33. sehan@microsoft.com