What is Innovation? How can cloud computing help you innovate? How can you make your applications smarter? Predictive? How can you interpret data and anticipate trends? With AWS Artificial Intelligence Solutions: Machine Learning, Rekognition, Polly; with serverless - Lambda, Step Functions.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
May 30, 2017
Innovating in the Cloud
Vancouver 2017
Sam Elmalak – Solutions Architect

2. Millions Of Active Customers Per Month

3. Largest Number Of Startup Customers

4. Largest Number Of Enterprise Customers

5. AWS Canada Customers

6. Broadest Ecosystem of ISVs

7. Cloud Has Become The New Normal

8. Fundamental Characteristics of the AWS Cloud
SECURITY
And critically…
Velocity Pace of
Innovation
Elasticity
AvailabilityLow Cost

9. What Are The Patterns Of This
New Normal?

10. Startups Are Breathing New Life Into Virtually Every Industry
Accommodation
Payments CommunicationTradingVisual Search
Healthcare
Teams
Commerce Education Customer Relations

11. Cloud Powered Enterprise Transformation

12. AWS Global Infrastructure
16 Regions – 42 Availability Zones – 74 Edge Locations
Region & Number of Availability Zones
AWS GovCloud (2) EU
Ireland (3)
US West Frankfurt (2)
Oregon (3) London (2)
Northern California (3)
Asia Pacific
US East Singapore (2)
N. Virginia (5), Ohio (3) Sydney (3), Tokyo (3),
Seoul (2), Mumbai (2)
Canada
Central (2) China
Beijing (2)
South America
São Paulo (3)
Announced Regions
France, China, Sweden (8 AZs)

13. Most Fully-Featured Technology Infrastructure Platform

14. Compute
Burstable (T2)
General Purpose (M4)
Dense Storage (D2)
Memory Intensive (R4)
Large Memory (X1)
High I/O (I3)
Compute Intensive (C5)
Graphics Intensive (G2)
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 Network AvailabilitySecurity & 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
Availability Zone architecture for
high operational resiliency
Synchronous data replication
between AZs within a region
Visibility into real-time network
performance
Broad IPv6 support
Multiple NICs per VM

15. New Capabilities Delivered Daily

16. 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
Compute Capabilities In The AWS Cloud

17. Bringing hardware
acceleration to all
F1 Instances
F1 Instance
With your custom logic
running on an FPGA
Develop, simulate, debug,
and compile your code
Package as
FPGA images
FPGA images available in AWS Marketplace

18. Genomics
research
Financial
analytics
Real time
video processing
Big data search
and analytics
Example Use Cases For F1 Instances

19. “Bigger” Data
+ Serverless
+ AI

20. The Ability to Understand Your Customers And
Business Better Through Analytics
Machine
Learning
Amazon
Machine Learning
Hadoop, Spark,
HBase, Hive, Presto,
Mahout, Pig, Zeppelin
Amazon
EMR
Elasticsearch
Amazon
Elasticsearch
Real-time
streaming data
Amazon
Kinesis
Data
Warehouse
Amazon
Redshift
Business
Intelligence
Amazon
QuickSight
Ad-hoc queries
against S3
Amazon
Athena

21. Amazon Athena:
Interactive Queries On Data In S3
No need to move
data to Athena
Query S3 directly
& right away
No infrastructure or
clusters to setup &
manage
Fast results
within seconds
Pay for just the
queries you run

22. Redshift, EMR & Athena:
The Right Tool For The Job
Data
Warehouse
Amazon Redshift
Hadoop, Spark
& Presto
Amazon EMR
Ad-hoc queries
against S3
Amazon Athena

23. Redshift Spectrum
Run queries directly on exabytes of data in Amazon S3

24. 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
Redshift Spectrum: Extend Redshift Queries To
Data In Amazon S3

25. 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
Redshift Spectrum Performance

26. 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
Redshift Spectrum Performance
Hive (1000 node clusters):
5 years

27. 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
Redshift Spectrum Performance

28. Microservices
“is a software architecture style in which complex
applications are composed of small, independent
processes communicating with each other using
language-agnostic APIs. These services are small,
highly decoupled and focus on doing a small task,
facilitating a modular approach to system-
building.” - Wikipedia
https://en.wikipedia.org/wiki/Microservices

29. Development transformation at Amazon: 2001+
<2001 >2001
monolithic
application +
teams
microservices + 2 pizza
teams

30. Event Driven Computing maps well to
Microservices
Microservices Architecture
GET /pets
PUT /pets
DELETE /pets
GET /describe/pet/$id
PUT /describe/pet/$id
Events

31. Event Driven Compute maps well to FaaS
GET /pets
PUT /pets
DELETE /pets
GET /describe/pet/$id
PUT /describe/pet/$id
Events Functions As A Service

32. Serverless application
SERVICES (ANYTHING)
Changes in
data state
Requests to
endpoints
Changes in
resource state
EVENT SOURCE FUNCTION
Node.js
Python
Java
C#

33. Using AWS Lambda
Bring your own code
• Node.js, Java, Python, C#
• Bring your own libraries
(even native ones)
Simple resource model
• Select power rating from
128 MB to 1.5 GB
• CPU and network
allocated proportionately
Flexible use
• Synchronous or
asynchronous
• Integrated with other
AWS services
Flexible authorization
• Securely grant access to
resources and VPCs
• Fine-grained control for
invoking your functions

34. Using AWS Lambda
Authoring functions
• WYSIWYG editor or
upload packaged .zip
• Third-party plugins
(Eclipse, Visual Studio)
Monitoring and logging
• Metrics for requests,
errors, and throttles
• Built-in logs to Amazon
CloudWatch Logs
Programming model
• Use processes, threads,
/tmp, sockets normally
• AWS SDK built in
(Python and Node.js)
Stateless
• Persist data using
external storage
• No affinity or access to
underlying infrastructure

35. Amazon S3 Amazon
DynamoDB
Amazon
Kinesis
AWS
CloudFormation
AWS CloudTrail Amazon
CloudWatch
Amazon
Cognito
Amazon SNSAmazon
SES
Cron events
DATA STORES ENDPOINTS
DEVELOPMENT AND MANAGEMENT TOOLS EVENT/MESSAGE SERVICES
Event sources that trigger AWS Lambda
… and a few more with more on the way!
AWS
CodeCommit
Amazon
API Gateway
Amazon
Alexa
AWS IoT AWS Step
Functions

36. Amazon API Gateway
Internet
Mobile Apps
Websites
Services
AWS Lambda
functions
AWS
API Gateway
Cache
Endpoints on
Amazon EC2
All publicly accessible
endpoints
Amazon
CloudWatch
Monitoring
Amazon
CloudFront
Any other AWS
service

37. Create a unified
API frontend for
multiple micro-
services
Authenticate and
authorize requests
to a backend
DDoS protection
and throttling for
your backend
Throttle, meter,
and monetize API
usage by 3rd party
developers
Amazon API Gateway

38. APPLICATION LIFE CYCLE IN AWS STEP FUNCTIONS
AWS STEP FUNCTIONS
Visualize in the
Console
Define in JSON Monitor
Executions

39. DEFINE IN JSON - VISUALIZE IN CONSOLE
40AWS STEP FUNCTIONS
{
”Comment”: “Hello World Example",
"StartAt” : "HelloWorld”,
"States” : {
"HelloWorld” : {
"Type” : "Task",
"Resource” :
"arn:aws:lambda:REGION:ACCOUNT_ID:function:FUNCTION_NAME”
,
"End” : true
}
}
}

40. MONITOR EXECUTIONS FROM THE CONSOLE
41AWS STEP FUNCTIONS

41. Common Lambda use cases
Web
Applications
• Static websites
• Complex web
apps
• Packages for
Flask and
Express
Data
Processing
• Real time
• MapReduce
• Batch
Chatbots
• Powering
chatbot logic
Backends
• Apps &
services
• Mobile
• IoT
</></>
Amazon
Alexa
• Powering
voice-enabled
apps
• Alexa Skills Kit
IT
Automation
• Policy engines
• Extending
AWS services
• Infrastructure
management

42. 43AWS STEP FUNCTIONS
Customers innovating with serverless applications

43. BusyBot
API Gateway
Lambda
DynamoDB
Waze for Trains
22,000 inputs per day
Trainline

44. Thomson Reuters Product Insight
To identify and define a solution for usage analytics tracking that enables product teams to
take ownership of the usage data collected. In addition to tracking and visualizing usage
data it had to;
1. Cross reference Usage
with Business data
4. Require Limited
Maintenance.
3. Auto Scale as data
flow fluctuates.
2. Follow TR Security &
Compliance rules.
5. Launch in 5 months.

45. SOLUTION

46. SOLUTION

47. SOLUTION

48. SOLUTION

49. SOLUTION

50. • Product Insight is live – adoption rate high.
• Tested 4,000 requests per second while targeting 5bn requests / month.
• Since March – very little maintenance required
• No Outages
• No Downtime
• Cloudwatch monitor everything.
• Latency – Data visible on chart within 10 seconds
• BrExit and US elections tested autoscaling.
• US elections ~16m events – normally ~ 6-8m events / day.
• UK EU referendum (BrExit) ~ 10m events – normally ~ 5m events / day
OUTCOME

51. 15-20 minutes of processing  now in seconds
2x order of magnitude for cost savings
https://www.youtube.com/watch?v=TXmkj2a0fRE
Nordstrom Recommendations

52. Amazon Alexa Skills
Your skill code

53. Your Skill Token
NoSQL database
Exchange LWA token for
AWS access key and secret key
Authenticate user
Receives OAuth token
Invoke
w/OAuth token
Identity provider
Use access key and secret key
to read/write items

54. Artificial Intelligence on AWS
P2Investment in
MXNet
Deep Learning AMI
and Template
Amazon Machine
Learning

55. Artificial Intelligence on AWS
P2Investment in
MXNet
Deep Learning AMI
and Template
Amazon Machine
Learning
Amazon Rekognition Amazon Polly Amazon Lex

56. Amazon AI
Bringing Powerful Artificial Intelligence To All Developers
Amazon
Rekognition
Amazon
Polly
Amazon
Lex

57. Amazon Rekognition:
Image Recognition And Analysis Powered By Deep Learning
Amazon Rekognition
Car
Outside
Daytime
Driving
Objects/Scenes
Female
Smiling
Sunglasses
Faces
Images In, Categories and Facial Analysis Out

58. Recognize, Search & Understand Images
Easy to use Batch
analysis
Real time
analysis
Low costContinually
improving

59. Amazon Polly: Text To Speech
Powered By Deep Learning
Amazon Polly
“The temperature
in WA is 75°F”
“The temperature
in Washington is 75 degrees
Fahrenheit”
Text In, Life-like Speech Out

60. Text To Speech Powered By Deep Learning
Returns an MP3
audio stream
Unlimited
replay
Fully managedLightning fast
responses

61. Amazon Lex:
Speech Recognition & Natural Language Understanding
Amazon Lex
Automatic Speech Recognition
Natural Language Understanding
“What’s the weather
forecast?”
Weather
Forecast

62. Amazon Lex:
Speech Recognition & Natural Language Understanding
Amazon Lex
Automatic Speech Recognition
Natural Language Understanding
“What’s the weather
forecast?”
Weather
Forecast
“It will be
sunny
and 75F”

63. Amazon Lex:
Speech Recognition & Natural Language Understanding
Amazon Lex
Automatic Speech Recognition
Natural Language Understanding
“What’s the weather
forecast?”
Weather
Forecast
“It will be
sunny
and 75F”
“It will be sunny
and 75 degrees
Fahrenheit”
Amazon Polly

64. Build Natural, Conversational Interactions In Voice & Text
Integrated
development in the
AWS console
Fully
managed
Trigger
Lambda
functions
Continually
improving
ASR & NLU
models
Enterprise
connectors
Multi-step
conversations

65. “Bigger” Data
+ Serverless
+ AI
== the future!

66. Predictive
financial models
Financial
simulations
Predictive models
for construction
Index people in
video footage
Deep learning in
customer service
Detecting and preventing
fraud with ML
Improved customer
experience
Real-time insights
from video
Bodycams Smart home
improvements
Predictions for
restaurant owners
Voice
recognition
Home price
predictions
Customers Achieving Precognition
Fundraising
chat bot
Computer
Vision APIs
Computer Vision For
Crowd Sourced Maps
Computer Vision For
Autonomous Driving
ML At Large
Scale
Luxury Real Estate
Purchase Predictions
Recommendation
Engine
Predictive Analytics
On Sports Plays
Image Recognition
Search

67. Amazon Marketplace: SaaS Contracts
AWS CodeStar
F1 Instances
Amazon DynamoDB Accelerator (DAX)
Redshift Spectrum
Amazon Rekognition: Content Moderation
Amazon Polly: Whispering Voices
Amazon Polly: Speech Marks
Amazon Lex GA
AWS X-Ray GA
There’s Never Been A Better Time To Build

68. aws.amazon.com/blogs/ aws
aws.amazon.com/new
@AWScloud
Learning more about AWS

69. T H A N K Y O U