[AWS Dev Day] 기조연설 – Olivier Klein AWS 신기술 부문 책임자, 정성권 삼성전자 수석

Build in 2019: Distributed, Open,
Data Centric and Artificially Intelligent
Olivier Klein
Head of Emerging Technologies, Asia-Pacific, AWS

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Portability
Data Centricity
Abstraction & Integration
Distributed Applications
Pace of Innovation
Changing Nature of
Software Development

FeedbackIdeas
Experiment
Innovation
Flywheel
Experiments power the engine of rapid innovation

Invention requires two things: the
ability to try a lot of experiments,
and not having to live with
the collateral damage of
failed experiments.
Andy Jassy
CEO, Amazon Web Services

Modern App Design lowers cost of experimentation

What changes have
to be made in this
new fast paced world?
Architectural patterns
Operational model
Software delivery

When the impact of change is small,
release velocity can increase
Monolith
Does everything
Microservices
Does one thing

Microservices with independent
functions, developers and SLAs

FUNCTION
API
API
MICROSERVICE
FUNCTION
EVENT
API
MICROSERVICE
EVENT
API
MICROSERVICE
APPLICATION
APIs and Event Messaging

Integration options from AWS
Queues
Simple
Fully-managed
Any volume
Pub/sub
Simple
Fully-managed
Flexible
Orchestration
Powerful
Fully-managed
Low code
Amazon
API Gateway
Amazon
SQS
Amazon
SNS
AWS
Step Functions
Connect
Efficient
Fully-managed
Real-time
Client-to-Service Service-to-Service Orchestration

API
MICROSERVICE
EVENT
API
MICROSERVICE
APPLICATION
Microservice Architecture

API
MICROSERVICE
EVENT
API
MICROSERVICE
APPLICATION
Microservice Architecture
CODE SDKs
DATASTORES

Amazon
DynamoDB
Amazon
ElastiCache
Key Value
In-Memory
Amazon
Neptune
Graph
Relational
Time-Series
Amazon
RDS
Amazon
Aurora
Amazon
DocumentDB
Document
Amazon
Timestream
Ledger
Amazon
QLDB
Choose the right data store for your microservice

Isn’t all of this very hard now that
we have lots of pieces to operate?

Don’t be a server hugger,
think serverless!

What is serverless?
No infrastructure provisioning,
no management
Automatic scaling
Pay only for value Highly available and secure

Focus on creating
business value
CUSTOMER
Remove heavy lifting with
serverless everything
AWS
Messaging & Orchestration
Compute
Physical Infrastructure
API
Business logic
Storage & Databases
Serverless provide great agility with the least effort

AWS
Lambda
AWS
Fargate
Amazon
API Gateway
Amazon
SNS
Amazon
SQS
AWS
Step Functions
COMPUTE
DATA STORES
INTEGRATION
Amazon Aurora
Serverless
Amazon
S3
Amazon
DynamoDB
AWS
AppSync

Serverless compute engine
for containers
Long-running
Bring existing code
Fully managed orchestration
AWS Fargate
Let’s focus on compute for a second
Serverless event-driven
code execution
Short-lived
All language runtimes
Data source integrations
AWS Lambda

New: Lambda Layers
Lets functions easily share code: Upload layer
once, reference within any function
Promote separation of responsibilities, lets
developers iterate faster on writing business logic
Built-in support for secure sharing through
ecosystem

Monolith development lifecycle
MonitorReleaseTestBuild
Developers
Delivery pipelines
Services

Microservice development lifecycle
Developers Services
Delivery pipelines

50,000,000
Amazon in 2014, average of 1.5 deployments per second

AWS CodeBuild
+ Third Party
AWS
CodeCommit
AWS
CodeBuild
AWS
CodeDeploy
AWS
X-Ray
AWS CodePipeline
MONITORDEPLOYTESTBUILDSOURCEAUTHOR
AWS Cloud 9
IDE Toolkits
Amazon
CloudWatch
AWS
CloudFormation AWS SAMMODEL
AWS Developer Tools for modern software delivery

Continuous security
Protecting your customers and your business in an agile world

IDENTITY
DETECTIVE
CONTROLS
INFRA
SECURITY
DATA
PROTECTION
INCIDENT
RESPONSE
Config
KMS Macie

Sungkwon Jung
Principal Architect
Samsung Electronics

클라우드
마이그레이션
마이크로서비스로
리팩토링
컨테이너 기반
클라우드 네이티브

마이그레이션을 위한 데브옵스 팀 역할
인프라스트럭처
엔지니어
데이터베이스
관리자

Database
Migration
Service
Amazon
Aurora
PostgreSQL
22 Weeks

Copy
Export
Import
Loading Initial
Data
CDC (Change Data Capture)
Corporate data
center
AWS Cloud

Containers / Serverless
Web Standards
Frameworks
APIs / Microservices
CLIENT
PORTABILITY
BACKEND
PORTABILITY
INFRA
PORTABILITY
SDKs
Cross-Platform
Native Apps

PHOTOGRAMMETRY

DEMO
Amazon
Sumerian
Pictures

AWS IoT Device SDK
Amazon FreeRTOS
AWS IoT Device Tester
AWS IoT Greengrass
AWS IoT Events
AWS IoT Analytics
AWS IoT SiteWise
AWS IoT Device Management
AWS IoT Things Graph
AWS IoT Core
AWS IoT Device Defender
Broad and deep IoT services

Robotics is undergoing fundamental
change in collaboration, autonomous
mobility, and increasing intelligence
• Logistics
• Construction
• Retail
• Hospitality
• Healthcare
Robots are being put to work every
day across many industries
• Agriculture
• Energy Management
• Oil and Gas
• Facilities Management
• Household chores

Development
Environment
Simulation
Environment
Fleet
Management
Cloud Extensions for ROS
and contribution to ROS2
AWS
Robomaker
Easily develop, test, and
deploy intelligent robotics
applications at scale

DEMO

What has Changed for me as a Developer?
Increasing amount of devices are being
connected with growing amounts of capabilities
Devices and apps do not serve a single purpose
anymore – entire ecosystems are building
through smart and connected systems
Global user base with the desire to interact
However, laws of physics haven’t changed –
latency is bound by physical distance

Strategies
Create globally distributed data stores
Deploy logic closer to the edge when
app is latency sensitive
Gracefully handle intermittent network
connectivity
Caching!

Relational: Amazon Aurora with cross-region replication
Non-relational: Amazon DynamoDB with cross-region replication
Objects: Amazon S3 with cross-region replication
Resolution: Amazon Route 53 with Latency Based Routing
Logic: AWS Lambda@Edge for code logic
Cache: Amazon CloudFront
Distributed Datastores and Applications

Automatically updates the data applications in
real time, and updates data for offline users as
soon as they reconnect
Makes it easy to build applications that deliver
responsive, collaborative user experiences
Schema designed using GraphQL and attached to
AWS resources
AWS AppSync – Handle Intermittent Networks

Edge Location
AWS Greengrass AWS Greengrass
Core
AWS Greengrass – Move your Logic to the Edge

Stop
throwing
data away
Make it
available to
more users
Arm users
with more
data processing
technologies
Think about data as an asset, not a cost

Glacier
Deep Archive
Lowest cost storage available in the
cloud…even lower than on-premises tape
No tape to manage
Designed for 99.999999999% durability
Recover data in hours vs. days/weeks
$0.00099/GB/month
Less than 1/4th the cost of Glacier

The ability to effectively and quickly derive
insights from your data lake is crucial to the
success of the business
Compute and storage should be decoupled
to accommodate for cost-effective yet fast
data growth
Data Transformation is not costly, don’t
worry too much about data structures

Cloud data lakes are the future
Customers want:
A single data store that is scalable & cost effective
To store data securely in various standard formats
To analyze their data in a variety of ways
Cloud Data Lake
Infrastructure
Decoupled Storage
& Compute Resources
Security & Governance
Data
Migration
Streaming
Services
Data
Warehouse
Big Data
Processing
Serverless Data
Processing
Real-time
Analytics
Operational
Analytics
Predictive
AnalyticsETL & Catalog
Data Management
Decouple compute and storage!

VISUALIZATION & ML ANALYTICS DATA LAKE DATA MOVEMENT
Amazon
QuickSight
Amazon
SageMaker
Amazon
Redshift
Amazon
EMR
AWS
Glue
Amazon
Athena
Amazon
Elasticsearch
Service
Amazon Kinesis
Data Analytics
Amazon
S3
Amazon Lake
Formation
AWS
Glue
AWS Database
Migration
Service
AWS
Snowmobile
AWS
Snowball
Amazon Kinesis
Data Firehose
Amazon Kinesis
Data Streams
Amazon Managed
Streaming for Kafka
Broadest and deepest data and analytics portfolio
AWS
Direct Connect
AWS
Storage Gateway
Amazon Kinesis
Video Streams
Amazon S3
Transfer
Acceleration
AWS Transfer
for SFTP
AWS
Snowball Edge

Station DepartureArrival

DepartureArrival Station

Computer Vision to identify empty
space and detect object misplacements

Put machine learning in the hands
of every IT professional
Our mission at AWS

KERAS
Frameworks
C5 C5n
P3 P3dn
Interfaces
Infrastructure
Vision Speech Languages Chatbots Vertical
AMAZON
SAGEMAKER AMAZON SAGEMAKER
GROUND TRUTH
AMAZON
SAGEMAKER RL
AWS MARKETPLACE
FOR ML
AMAZON
SAGEMAKER NEO
AWS
DEEPRACER
AWS
DEEPLENS
AMAZON ELASTIC INFERENCE
AWS INFERENTIA
AWS GREENGRASS
AMAZON
PERSONALIZE
AMAZON
FORECASTAMAZON
TEXTRACT
AMAZON
REKOGNITION
AMAZON
LEX
AMAZON
POLLY
AMAZON
TRANSCRIBE
AMAZON
COMPREHEND
AMAZON
TRANSLATE
AI Services
ML Services
ML Frameworks +
Infrastructure

Transcribe 8Khz call recordings
with high accuracy
Analyze the text with Amazon
Comprehend
Visualize results on Amazon
QuickSight
Call center insights
Amazon
Transcribe
Amazon
Comprehend
Amazon
Connect
Amazon
Quicksight

Amazon SageMaker
Fast & accurate data
labeling
Built-in, high
performance
algorithms &
notebooks
BUI L D
1
One-click
training and
tuning
T R A I N
Model optimization
2
Fully managed
hosting with
auto-scaling and
elastic inference
One-click
deployment
D E P L O Y
3
Custom machine learning for your business

Image Classification and Object Detection

Understand physical security footage

AWS is framework agnostic
Choose from popular frameworks
Run them fully managed Or run them yourself

Running inference
in production
Inference
Training
Infrastructure
costsDRIVES THE MAJORITY OF COST
FOR MACHINE LEARNING

COMING LATE 2019
Add GPU acceleration to any Amazon
EC2 instance for faster inference at
much lower cost (up to 75% savings)
Amazon
Elastic Inference
Custom high performance machine
learning inference chip that will
reduce the cost of inference by 10X
AWS Inferentia

How do you teach machine learning models to
make decisions when there is no training data?

Supervised learning
Unsupervised learning
Reinforcement Learning

Learn by
interacting with
the real world
Model problem
as a simulation
environment
Trial and error
Observe results
Optimise learning
strategy to maximise
long-term reward

Vehicle routing
Objective Fulfill customer orders
S T AT E Current location, distance from homes …
A C T I O N Accept, pick up, and deliver order
R E W A R D Positive when we deliver on time
Negative when we fail to deliver on time
Applicable in many domains and industries

Amazon
SageMaker RL
New machine learning capabilities in
Amazon SageMaker to build, train and
deploy with reinforcement learning
Fully managed RL
algorithms
TensorFlow, MXNet,
Intel Coach, and Ray RL
2D and 3D simulation
environments via OpenGym
Simulate with Sumerian and
AWS RoboMaker
Example notebooks and
tutorials

DEMO
Amazon
Sumerian
Amazon
Sagemaker RL

Experiment quickly using modern application
architectures and cloud services
Deploy globally or close to the edge, and build
on portable standards
Data and intelligence is an asset – AWS makes it easy and
cost-effective to use cloud data lakes and ML
Focus on what’s core to your business - leave the
heavy lifting to someone else
In Closing

여러분의 피드백을 기다립니다!
#AWSDEVDAYSEOUL

[AWS Dev Day] 기조연설 – Olivier Klein AWS 신기술 부문 책임자, 정성권 삼성전자 수석

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[AWS Dev Day] 기조연설 – Olivier Klein AWS 신기술 부문 책임자, 정성권 삼성전자 수석