amazon 인공 지능(ai) 서비스 및 aws 기반 딥러닝 활용 방법 - 윤석찬 (aws,...

© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

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•https://www.youtube.com/watch?v=gQpMDdJmbNs

© Jeff Dean, Trends and Developments in Deep Learning Researchhttp://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research

Accuracy

Scale (data size, model size)

1980s and1990s

neural networks

other approaches

© Jeff Dean, Trends and Developments in Deep Learning Researchhttp://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research

more computeAccuracy

Scale (data size, model size)

neural networks

other approaches

1980s and1990s

© Jeff Dean, Trends and Developments in Deep Learning Researchhttp://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research

more computeAccuracy

Scale (data size, model size)

neural networks

other approaches

Now

© Jeff Dean, Trends and Developments in Deep Learning Researchhttp://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research

2016

3% errors

2011

5% errors

humans

26% errors© Jeff Dean, Trends and Developments in Deep Learning Research

http://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research

http://techneedle.com/archives/29063

"아마존 상품 배송 선반에는 물건이뒤죽박죽 보관되고 있다. 예를 들면영화 DVD, 로션, 피클이 한 칸에 보관되어 있다. 자투리 공간 없이 창고공간을 100% 활용할 수 있고, ‘이상품군은 어디에 보관해야 한다’ 등배워야 할 내용이 줄어든다. 게다가실수로 잘못 꺼내는 일도 적다. 상품보관을 위한 기술은 컴퓨터 비전과실내 위치 추적 등의 시스템을 활용한다"

Original image Activation map Binary map

2.0

1.0

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https://www.amazon.com/b?node=16008589011

The image part with relationship ID rId7 was not found in the file.

The image part with relationship ID rId7 was not found in the file.

Amazon Echo sales up 9X compared to last year, company says in holiday roundup of 2016 winter sold “millions” of Echo devices.

https://www.wired.com/2017/01/ces-alexa-in-everything

Create Great Content:

ASK is how you connectto your consumer

Supported by two powerful frameworks

A L E X AV O I C ES E R V I C E

UnparalleledDistribution:

AVS allows your contentto be everywhere

Lives In The Cloud

Automated SpeechRecognition (ASR)

Natural Language Understanding (NLU)

Always Learning

A L E X AS K I L L SK I T

Alexa Skills Kit 구조도

Amazon Alexa

Service

Developer’s Application

Service

Amazon’s Developer

Portal애플리케이션, 사용자의도,샘플데이터, 개발자서비스등정보전달

사용자의도를서비스로전달

GUI 카드를알렉사앱에전달

오디오를서비스로전송오디오결과를렌더링해서재생

텍스트결과및GUI 카드정보전달

Alexa Skill 만들기 – AWS Lambda 기반 VoiceOps

https://www.youtube.com/watch?v=azKYe4IWTxA

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https://github.com/alexa/alexa-avs-sample-app/wiki/Raspberry-Pi

16 years = 140,160 hours≈14,016 hours of speech

고객 기술지원

영업 지원

고객지원

프로페셔널 컨설팅

교육 및인증

보안 및 빌링시스템

파트너생태계

솔루션아키텍트

엔터프라이즈

가상 테스크톱

기업용 공유도구

기업용 이메일

백업 및 복구

글로벌 리전 (Region) 가용 영역 (AZ) 콘텐츠 배포 지점(Edges)

인프라

기본 서비스컴퓨팅VMs, Auto-scaling, Load Balancing, Containers, Cloud functions

스토리지Object, Blocks, File, Archival, Import/Export

데이터베이스Relational, NoSQL, Caching, Migration

네트워킹VPC, DX, DNS

콘텐츠 배포 (CDN)

하이브리드환경

데이터백업

통합 앱 개발 환경

전용 회선연결

통합 인증

통합 리소스관리

네트워크통합

서비스접근 제어

사용자인증 관리

암호 키 관리 및 저장

모니터링로그

기업 자원설정 및 보고

리소스 사용량 및 감사

보안 및 규정 준수

기업 내규정 준수 웹 방화벽

비지니스애플리케이션

비지니스인텔리전스 데이터베이스DevOps

도구네트워킹보안 스토리지

IoT

규칙 엔진

디바이스쉐도우

디바이스SDK

디바이스레지스트리

디바이스게이트웨이

개발 및 운영 도구모바일 서비스앱 서비스데이터 분석

데이터웨어하우스

Hadoop/Spark

실시간 데이터저장

머신 러닝

Elastic Search 서비스

알람 및 큐서비스

워크 플로우

풀텍스트검색

Email 전송

동영상 변환

원 클릭 앱 개발

모바일 인증

기기 동기화

모바일 앱테스트

푸시 알림

DevOps 리소스관리

앱 라이프사이클관리 도구

콘테이너 서비스

클라우드 함수

리소스 템플릿

API Gateway

실시간 데이터분석

비지니스 인텔리전스

모바일 분석

모바일 허브

마켓 플레이스

Amazon Machine Learning

P2 InstanceDeep Learning

AMI and templateInvestment in

MXNet

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vCPU 32 / RAM 488GB GPU 8 x NVIDIA K80

p2.8xlarge= $7.2 per hour

x 20vCPU 640 GPU 160

p2.8xlarge x 20= $144 per hour

$aws ec2-run-instances ami-b232d0db--instance-count 20--instance-type p2.8xlarge--region us-east-1

$aws ec2-stop-instances i-10a64379 i-10a64280 ...

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http://bit.ly/deepami

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https://github.com/awslabs/ecs-deep-learning-workshop

•ü https://github.com/dmlc/mxnet/tree/master/dockerü https://hub.docker.com/r/kaixhin/cuda-mxnet/

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https://www.slideshare.net/AIFrontiers/scaling-deep-learning-with-mxnet

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1 2 3

- 데이터 소스로 부터DataSource 생성

- 데이터 내용과스키마 확인

- 모델링 진행

- 생성된 모델의품질 확인

- 모델에 해석상세 조절

--

컴퓨터비전 API

온라인부정지불감시

클라우드소싱기반지도서비스

자율주행컴퓨터비전분석

대용량기계학습 부동산구매예측분석 동영상추천엔진개발 고객트래픽분석

스포츠플레이예측 이미지인식기반검색

Zestimate 서비스(Apache Spark 활용)

보험리스크분석

Amazon Machine Learning 기반 모델 훈련 및 예측

https://youtu.be/4loLsXKlJUM

Flower

ChairCoffee Table

Living Room

Indoors

Maple

Villa

Plant

Garden

Water

Swimming Pool

Tree

Potted Plant

Backyard

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Female

Happy

Smiling

Male

No Facial Hair

Happy

Female

Sad

No Eyeglasses

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f7a3a278-2a59-5102-a549-a12ab1a8cae8 &v1

02e56305-1579-5b39-ba57-9afb0fd8782d&v2

Face ID & vector<float>Face

4c55926e-69b3-5c80-8c9b-78ea01d30690&v3

tran

sfor

med

stor

ed

{f7a3a278-2a59-5102-a549-a12ab1a8cae8, 02e56305-1579-5b39-ba57-9afb0fd8782d,4c55926e-69b3-5c80-8c9b-78ea01d30690}

IndexFace Collection

Amazon Rekognition 살펴 보기

https://youtu.be/yrldn0JciIU

Training

Conv 1 Conv 2 Conv n

…

…

Feature Maps

Fully Connected

Layer

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•§ (24 47 )§ (Amazon Lex )§ SSML§ ,§ ( 2.5 )

•

Amazon Polly

“The temperature in WA is 75°F”

“The temperature in Washington is 75 degrees Fahrenheit”

"We live for the music", live from the Madison Square Garden.

"We live(리브) for the music", live(라이브) from the Madison SquareGarden.

<speak>

My name is Kuklinski. It is spelled

<prosody rate='x-slow'><say-as interpret-as="characters">Kuklinski</say-as>

</prosody>

</speak>

My daughter’s name is Kaja.<lexeme>

<grapheme>Kaja</grapheme><grapheme>KAJA</grapheme><phoneme>"kaI.@</phoneme>

</lexeme>

Amazon Polly 살펴 보기

https://youtu.be/yrldn0JciIU

RSS Feed Amazon Polly

Amazon CloudWatch

Amazon S3AWS Lambda

1. Trigger

2. Check

3. Content

4. Text 5.Audio

6.Audio

https://github.com/awslabs/amazon-polly-sample

Amazon RekognitionAWS SDK

for iOS

Amazon Cognito Amazon Polly

Amazon S3 Bucket

Skate, Car, Street, Parking, Town

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FacebookMessengerMobile

Book Hotel

“Book a Hotel inNYC”

Hotel Booking

New York City

Natural Language Understanding

Intent/Slot Model

UtterancesHotel BookingCity New York City

CheckIn Nov 30th

CheckOut Dec 2nd

“Your hotel is booked for Nov 30th”

Polly Confirmation: “Your hotel is booked for Nov 30th”

The image part with relationship ID rId3 was not found in the file.“Can I go ahead

with the booking?

a

in NYC

Automatic Speech Recognition

Amazon Lex

Automatic Speech Recognition (ASR)

Natural Language Understanding (NLU)

Same technology that powersAlexa

Cognito CloudTrail CloudWatch

AWS Services

Action AWSLambda

Authentication &Visibility

SpeechAPI

Language API

Fulfillment

End-Users

Developers

Console

SDK

Intents, Slots, Prompts, Utterances

Input: Speech or Text

Multi-Platform Clients: Mobile, IoT, Web,

Chat

API

Output:Speech (via PollyTTS) or Text

BookHotel

Amazon Lex를 통한 호텔 예약 챗봇 만들기

https://youtu.be/c1YC8mIiWh0

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Amazon Rekognition

AWS SDKfor iOS

Amazon CognitoAmazon Polly

AWSLambda

Amazon S3 Bucket

Amazon SNS

Amazon Lex

Amazon S3 Bucket

à

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AmazonRekognition

AmazonLex

AWS IoT

AWSLambda

Amazon S3

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P2Amazon

Machine Learning Deep LearningAMI and template

Investment inMXNet

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윤석찬아마존웹서비스코리아, 테크에반젤리스트

channyun@amazon.comhttp://bit.ly/awskr-feedback