AWS Summit Atlanta Keynote

1. Stephen Orban
Global Head of Enterprise Strategy
Amazon Web Services
@stephenorban

2. 600+
attendees
12
technical sessions
W E L C O M E

3. A Quick Update On The AWS Business…

4. $16B Revenue Run Rate
(Trailing 12 months, as of Q2 2017)
42% YoY Growth
(Q2 2016 vs. Q2 2017)

5. Millions Of Active Customers Per Month

6. Startup Customers

7. Enterprise Customers

8. Public Sector Customers

9. Broadest Ecosystem Of Consulting Partners

10. Broadest Ecosystem Of ISVs

11. 135,000+ active customers
3,800+ software listings
Over 1,200 participating ISVs
AWS Marketplace
Quickly find, buy, and deploy applications on AWS

12. AWS Positioned as a Leader in the Gartner Magic Quadrant for
Cloud Infrastructure as a Service, Worldwide*
AWS is positioned
highest in execution
and furthest in vision
within the Leaders
Quadrant
*Gartner, Magic Quadrant for Cloud Infrastructure as a Service, Worldwide, Leong, Lydia, Bala, Raj, Lowery, Craig, Smith, Dennis, June 2017 G00315215
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from AWS : http://www.gartner.com/doc/
reprints?id=1-2G2O5FC&ct=150519&st=sb
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications
consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability
or fitness for a particular purpose.

13. Where Are We In The Cloud’s Evolution?
New Normal
In 2014

14. Where Are We In The Cloud’s Evolution?
New Normal
In 2014
Control Over
Your Own Destiny
In 2015

15. With AWS, It Can Feel Like You Have Been Given
S U P E R P O W E R S

16. S U P E R S O N I C
S P E E D

17. Pace Of Innovation:
New Capabilities Daily

18. Most Robust, Ful ly-Featured Tec hnology Infrastructure Platform

19. Most Robust, Ful ly-Featured Tec hnology Infrastructure Platform

20. Compute
Burstable (T2)
General Purpose (M4)
Dense Storage (D2)
Memory Intensive (R4)
Large Memory (X1)
High I/O (I3)
Compute Intensive (C5)
Graphics Intensive (G3)
General Purpose GPU (P2)
FPGAs (F1)
Simple VPS (Lightsail)
RDS For MySQL
RDS For PostgreSQL
RDS For MariaDB
RDS For Oracle
RDS For SQL Server
RDS For Aurora
Heterogeneous migrations
with no downtime
Broad Services With Deep Functionality
Databases Security & Access
Fully-managed DDoS Protection
WAF with instant threat mitigation
Dedicated HSMs
Graphic Policy Simulator
Identify, Location, and Time Policies
Individual API Call Policies
Key Usage Auditing

21. Broad Services With Deep Functionality
Compute
Burstable (T2)
General Purpose (M4)
Dense Storage (D2)
Memory Intensive (R4)
Large Memory (X1)
High I/O (I3)
Compute Intensive (C5)
Graphics Intensive (G3)
General Purpose GPU (P2)
FPGAs (F1)
Simple VPS (Lightsail)
RDS For MySQL
RDS For PostgreSQL
RDS For MariaDB
RDS For Oracle
RDS For SQL Server
RDS For Aurora
Heterogeneous migrations
with no downtime
Databases Security & Access
Fully-managed DDoS Protection
WAF with instant threat mitigation
Dedicated HSMs
Graphic Policy Simulator
Identify, Location, and Time Policies
Individual API Call Policies
Key Usage Auditing

22. Elastic GPUs On EC2
P2M4 D2 X1 G2T2 R4 I3 C5
General Purpose
GPU
General Purpose
Dense storage Large memory
Graphics intensiveMemory intensive High I/O
Compute intensiveBurstable
Lightsail
Simple VPS
F1
FPGAs
A B r o a d S p e c t r u m O f C o m p u t e C a p a b i l i t i e s I n T h e AW S C l o u d

23. Secure financial
services
On-demand,
customized
entertainment
3.2 million
online razor
subscribers
Transforming
oil and gas
Migrated 300
servers to AWS
in 6 months
Unprecedented
scale and speed
Cut development
cycles by >50%
Finding patterns in
vast amounts of
data
Reduce analysis
from four months
to four hours
Financial trading with
strong built-in
security and
compliance
Achieving Supersonic Speed
Migrated 300
servers to AWS
in 6 months
Directly upload
high bit rate video

24. Scott Haley
Head of Solutions Engineering

33. WHAT IS A RENDER?

60. I N V I S I B I L I T Y

61. Microservices: Small, Independent Building Blocks
Independent
scaling
Independent security
and permissions
Independent
deployments
Improved
fault isolation

62. The Rise Of Microservices Has Been Enabled By Containers
Monolithic
application
Services Microservices

63. Challenges With Running Containers Today
Cluster
management
Scaling Fault
tolerance
Securing your
environment

64. Amazon EC2 Container Service (ECS)
Container
management
Container
registry
No
infrastructure
management
Access to
EBS, ELB,
CloudWatch
Integration
with IAM
Batch and long
running task
scheduling
Multi-AZ
aware
The best way to run your containers in production

66. What Do Serverless Applications Look Like?
No provisioning or
management
Scales with usage Never pay for idle Fault tolerance
built in

67. AWS Has The Complete Container Platform To
Deliver And Operate Serverless Applications
Application
modeling
framework
SAM
CI/CD
CodePipeline
Diagnostics
X-Ray
Monitoring
CloudWatch
SDKs and tools
SDKs
IDE Plugins
Third-party
Serverless Framework
Sparta Apex, Loggly,
Splunk, SumoLogic etc.
State management
Step
Functions
Notifications
SNS
Queues
SQS
Streaming analytics
Kinesis
API proxy
API Gateway
Compute
Lambda
Storage
S3
Database
DynamoDB
Networking
VPC
Security
IAM
CodeBuild
CORE BUILDING BLOCKS
DEVELOPER TOOLS
APPLICATION SERVICES
CloudFormation
Build and
test
Templates

68. Run code without
thinking about
servers
Pay only for the
compute time you
consume
Integrated with
12 services
Run your code based
on API calls or
triggers from AWS
services
AWS Lambda
Serverless compute

69. Customer Success With AWS Lambda

70. Select image converter
RAW to JPEG RAW to PNGRAW to TIFF
Load in Database
Start
End
Unsupported image type
Process photo
Resize image
Start
End
Extract metadata Facial recognition
Load in Database
Parallel steps Branching steps
AWS Step Functions
Sequential steps
Start
U p l o a d R AW f i l e
D e l e t e R AW f i l e
End
Coordinate the components of distributed applications using visual workflows

71. Amazon DynamoDB Accelerator (DAX): 10x Faster
Cache
DAX
Amazon
DynamoDB
Microseconds response time
Millions of requests per second
Fully managed, highly available
No need to rewrite your applications
Your
applications

72. Real-time social
experience
Reduced time to
service by 300%
Serverless for app
and website
Assess trending
stories 50% faster
10x revenue
growth
Processes 4,000
requests per second
Retail marketing
application
Increased efficiency
and scalability
Achieving Invisibility
Microservice
architecture manages
usage spikes

73. Adam Raymer
GE Digital
VP - External Cloud

74. 295K
Employees
170+
Countries 123B
2016
revenue
125
year
history
2000+
Cloud
Applications
55+
AWS
Services In
Use
4000+
Locations
POWER BAKER
HUGHES, A GE
COMPANY
AVIATIONENERGY
CONNECTIONS
RENEWABLES LIGHTING &
CURRENT
CAPITAL
TRANSPORTATION HEALTHCARE
GE DIGITAL

75. Infrastructure Mission:
Be Invisibly Ubiquitous

76. Plan Cleared

77. Liftoff AdjustPlan Cleared

78. Placeholder confidentiality disclosure. Edit or delete from master slide if not needed.
“There's a way to do it better - find it.”
- Thomas A. Edison

80. F L I G H T

81. Old World Databases
You’ve got
mail!
AUDIT
Very expensive Proprietary Lock-in Punitive
licensing

82. It’s Why Customers Are Moving To Open Database Engines

83. Amazon Aurora
Up to 5x
performance
of high-end
MySQL
Highly
available
and durable
MySQL
compatible
1/10th the cost of
commercial grade
databases
Fastest
growing
AWS service,
ever
Speed and availability of commercial databases with cost-effectiveness of open source

84. Migrating Databases To AWS
34,000+
databases migrated
Migrate between
on-prem and AWS
Migrate between
databases
Automated schema
conversion
Data replication for
zero downtime migrations

85. Amazon Aurora PostgreSQL-Compatible Edition
1/10th the cost of
commercial grade
databases
Fully PostgreSQL
compatible
Several times
better performance
than typical
PostgreSQL
database
Scalable,
durable and
secure
Migrate from
RDS for
PostgreSQL

86. 1/10th the cost of
commercial grade
databases
Fully PostgreSQL
compatible
Several times
better performance
than typical
PostgreSQL
database
Scalable,
durable and
secure
Migrate from
RDS for
PostgreSQL
Now available in open preview
Amazon Aurora PostgreSQL-Compatible Edition

87. Examples Of Customer Success Using Aurora
Lodging inventory
system
Scale for large
events
Billions of financial
transactions
Petabytes of streams
and playback artifacts
>2 billion rows
Millions of
transactions per day
Reduced
operational burden

88. Databases, And Servers, And Applications, Oh My!

89. Typical Migration Engagement
Migration Readiness and Planning (MRP) Application Migrations
Build the foundation:
Operational Readiness
‒ Cloud Center of Excellence
‒ Landing Zone
‒ Operations Model
‒ Security and Compliance
Clear business case
Migration plan
Achieve business objectives:
Scale migration to meet the objectives
outlined in your business case
‒ People
‒ Process
‒ Technology
Application Design
Migration Preparation
& Business Case
Portfolio Discovery
& Planning
Migration
& Validation
Operate &
Optimize
Migration Readiness
Assessment
Determine Readiness:
Current State
People
Platform
Operations
Security
Clear Business case
Clear Migration plan

90. A single location to track migrations
across multiple AWS services and
partner solutions
AVAILABLE
AWS Migration Hub

91. Start your
journey with a
few clicks

92. Track progress
from a single
location

93. View status from
multiple tools

94. Summary of
in-progress
migrations

95. X - R A Y V I S I O N

96. AWS Has Made Petabyte-Scale Analytics
Accessible To Companies Of All Sizes
Interactive query service
Amazon Athena
Hadoop, Spark and Presto
Amazon EMR
Data warehousing
Amazon Redshift

97. Amazon Athena
Query S3 directly and right away
No infrastructure or clusters to setup and manage
Fast results within seconds
Pay for just the queries you run
Interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL

98. Amazon EMR
Fully customizable
clusters
Code your own
applications
Hadoop, Spark,
Presto, HBase
Analyze large data sets using popular big data processing frameworks

99. Amazon Redshift
Highly structured Complex queries
across very large
databases
Pull together data
from different sources
20x faster than
ad-hoc query
services
Petabyte-scale data warehousing for complex queries and super-fast performance

100. Customers Love Redshift

101. SunTrust Bank, Inc.
ranks 9th amongst
U.S. commercial
banks, with $207
billion in total assets
as of June 30, 2017

102. SunTrust
Flag

103. SunTrust Footprint
Dimension Value (Rank
[1]
) Dimension Value (Rank)
Assets $207 billion (9th) Bank branches ~1,350 (7th)
Deposits $160 billion (8th) ATMs ~2,200 (7th)
Loans $144 billion (6th) Clients Approx 4.7 million
Market Cap.
[2]
$27 billion (9th) Teammates
[3]
~24,000
Footprint Business
• Consumer Banking
• Business Banking
• Commercial Banking
• Private Wealth Management
• Division Wealth Management Medical
• Institutional Investment Solutions
• International Wealth Management
Expanded Business
• Commercial Real Estate
• Corporate Banking
• Private Wealth Management
• Genspring
• Sports & Entertainment Group
National Business
• Consumer Lending
• Investment Banking
• Mortgage
[1] As of 6/30/2017, rank amongst U.S. commercial banks, excluding non-
traditional commercial banks
[2] As of 7/31/2017
[3] Full-time equivalent employees at 12/31/16, per 20156Annual Report

104. SunTrust is adopting AWS capabilities to improve
agility of our Business Segments, align to
emerging trends across the industry, and achieve
performance commitments

105. The SunTrust is using AWS to improve flexibility,
transform the organization and infrastructure, and
enabling analytics with real time data.

106. Working with AWS
Breadth and
depth of services
Favorable
economics
Application
rationalization and
savings through data
center consolidation
Redundancy and
disaster recovery
capabilities at scale

107. But Customers Are Storing Even More Data In Amazon S3
Data Lake
Amazon Redshift
Redshift Query Engine
Redshift Data
Amazon S3
Petabytes Exabytes

108. Generally available
Redshift Spectrum
Run queries directly on exabytes of data in Amazon S3
Introducing

109. Redshift Spectrum: Extend Redshift Queries To Data In Amazon S3
Amazon Redshift
Query S3 directly or join data
across Redshift and S3
Support for CSV, JSON, ORC,
and Parquet data formats
Scale Redshift compute and
storage separately
Redshift Query
Engine
Redshift Data
Data Lake
Amazon S3

110. Redshift Spectrum Performance
Complex query
against exabyte dataset
4 tables (1 S3, 3 local), 8
filters, 3 joins, 4 group by
columns, 1 order by, 1 limit,
1 aggregation, 1 function
and 2 casts

111. Redshift Spectrum Performance
Hive (1000 node clusters):
5 years
Complex query
against exabyte dataset
4 tables (1 S3, 3 local), 8
filters, 3 joins, 4 group by
columns, 1 order by, 1 limit,
1 aggregation, 1 function
and 2 casts

112. Redshift Spectrum Performance
Spectrum:
155 seconds
Hive (1000 node clusters):
5 years
Complex query
against exabyte dataset
4 tables (1 S3, 3 local), 8
filters, 3 joins, 4 group by
columns, 1 order by, 1 limit,
1 aggregation, 1 function
and 2 casts

113. Tedious, “fiddly”
boilerplate code
Understand
what data you
have and where
Done
repeatedly,
reliably, and
at scale
Resource-
intensive
and irregular
Extract, Transform, Load: Ubiquitous And Tedious

114. AVAILABLE TODAY
AWS Glue
Fully managed Data Catalog
and ETL service

115. AWS Glue
Fully managed Data Catalog and ETL service
Discovers
and catalogs
your data
Schedules
and runs your
ETL jobs
Generates
customizable
transformation
code
Serverless -
provisions and
manages your
infrastructure

116. Customers Using AWS Glue
Inferring,
classifying, and
transforming data
Onboard new
datasets and test
and run ETL jobs
Using IoT and Machine
Learning on connected
products
Cut
DevOps
time in half

117. P R E C O G N I T I O N

118. Machine Learning At Amazon.com
Retail
Demand Forecasting
Vendor Lead Time
Prediction
Pricing
Packaging
Substitute Prediction
Customers
Recommendation
Product Search
Product Ads
Shopping Advice
Customer Problem
Detection
Catalogue
Browse-Node
Classification
Meta-data Validation
Review Analysis
Product Matching
Text
In-Book Search
Named-entity Extraction
Summarization/Xray
Plagiarism Detection
Seller
Fraud Detection
Predicitve Help
Seller Search &
Crawling
Images
Visual Search
Product Image
Enhancement
Brand Tracking

119. Real Machine Learning Happening On AWS
Computer Vision APIs
Detect Online
Payment Fraud
Computer Vision For
Crowd Sourced Maps
Computer Vision For
Autonomous Driving
ML At Large Scale
Luxury Real Estate
Purchase Predictions
Recommendation Engine Forecast Customer Traffic
Predictive Analytics
On Sports Plays
Image Recognition Search Zestimate (using Apache Spark)
Insurance

120. Building Smarter Applications
See Speak UnderstandHearPredict

121. Building Smarter Applications
See Speak UnderstandHearPredict
Amazon
ML
Deep
learning AMI

122. Train Custom Predictive Models With Amazon Machine Learning
Data
(S3 and Redshift)
Custom
prediction API
Amazon ML

123. Train Custom Predictive Models With Amazon Machine Learning
In the cloud
At the edge
Data
(S3, EFS, EBS)
Custom deep
learning model
Deep learning AMI

124. Amazon
Rekognition
Building Smarter Applications
Amazon
ML
Deep
learning AMI
SeePredict Speak UnderstandHear

125. “See” Objects, Scenes, And Faces With Amazon Rekognition
City 91.4%
Downtown 91.4%
Metropolis 91.4%
Urban 91.4%
Building 56.2%
High Rise 56.2%
Dock 55.6%
Dawn 52.4%

126. “See” Faces And Emotions With Amazon Rekognition
Looks like a face 99.9%
Appears to be male 99.9%
Age range 27–44 years old
Smiling 99.2%
Appears to be happy 99.5%
Not wearing eyeglasses 99.9%

127. Amazon
Polly
Building Smarter Applications
Amazon
Rekognition
Amazon
ML
Deep
learning AMI
See SpeakPredict UnderstandHear

128. Generate Lifelike Speech With Amazon Polly
47 voices 24 languages
“The temperature in
Washington is 75 degrees
Fahrenheit”
“The temperature
in WA is 75°F ”
Amazon
Polly

129. Generate Lifelike Speech With Amazon Polly
Customize what
is said
Customize how
it’s said

130. Generate Lifelike Speech With Amazon Polly
Whispering
voices
Customize what
is said
Customize how
it’s said

131. Generate Lifelike Speech With Amazon Polly
Whispering
voices
Customize what
is said
Customize how
it’s said
Speech
marks

132. Building Smarter Applications
Amazon
Polly
Amazon
Rekognition
Amazon
ML
Deep
learning AMI
See SpeakPredict UnderstandHear

133. Natural language
understanding
Automatic
speech recognition
Amazon
Lex
Amazon
Lex
Amazon
Polly
Amazon
Rekognition
Amazon
ML
Deep
learning AMI
See SpeakPredict
Building Smarter Applications

134. Amazon Lex
Speech recognition and natural language understanding
Automatic speech recognition
Natural language understanding
“What’s the weather
forecast?”
Weather
forecast
Amazon Lex

135. Amazon Lex
Speech recognition and natural language understanding
“It will be
sunny
and 75ºF”
Automatic speech recognition
Natural language understanding
“What’s the weather
forecast?”
Weather
forecast
Amazon Lex

136. “It will be sunny
and 75 degrees
Fahrenheit”
Amazon Polly
Amazon Lex
“It will be
sunny
and 75°F”
Automatic speech recognition
Natural language understanding
“What’s the weather
forecast?”
Weather
forecast
Speech recognition and natural language understanding
Amazon Lex

137. Amazon Lex
Integrated
development in the
AWS Console
Trigger Lambda
functions
Continually improving
ASR and NLU models
Multi-step
conversations
Enterprise
connectors
Fully
managed
Salesforce Microsoft Dynamics
Marketo Zendesk
Quickbooks Hubspot
Speech recognition and natural language understanding

138. Securing Sensitive
Data Is Job Zero

139. Identifying And Protecting Sensitive Data
Can Be Challenging
Manual
process
Time-
consuming
Inaccurate Expensive Growing
volumes

140. NOW AVAILABLE
Amazon Macie
Automatically discover, classify, and protect
sensitive data in AWS using Machine Learning

141. Understand where
sensitive data is
located and how it
is accessed
Automatically
monitor for
anomalies
Automatically
discover and
classify your data
Alert your security
team when
anomalies are
detected
Introducing Amazon Macie

142. Some Of The Customers Using Amazon AI In Production Today

143. Improving Government Transparency
Using Amazon Rekognition

145. Automating Footage Tagging
With Amazon Rekognition
Index against 99,000 people
Saving ~9,000 hours a year in labor
Built in 3 weeks

146. In Conclusion…

147. I M M O R T A L I T Y

148. Accommodation
Payments CommunicationTradingVisual search
Healthcare
Teams
CPG Education Customer relations
Startups Are Breathing New Life Into Virtually Every Industry

149. Digital Transformation Is Key To Survival For Enterprises

150. AWS Pace Of Innovation: New Capabilities Daily
There Has Never Been A Better Time To Build

151. Thank You & Go Build!
http://amzn.to/considering-mass-migration
http://amzn.to/enterprise-cloud-best-practices
http://amzn.to/cloud-stages-of-adoption
http://amzn.to/culture-eats-strategy-for-breakfast
@stephenorban
orbans@amazon.com