아마존닷컴은 쇼핑 상품 추천, 배송 및 물류 예측 등에 기계 학습 기술을 활용해 왔으며, 최근 프라임 서비스를 위한 음악, 이미지, 영상 인식, 무인 매장인 아마존고 및 음성 비서 서비스인 알렉사에 딥러닝 기술을 활용하고 있다. 본 세션에서는 이러한 주요 딥러닝 활용 기술 사례를 알아보고, AWS 클라우드를 통해 제공하는 이미지/영상 인식, 음성 인식 및 합성, 기계 번역, 자연어 처리 등 다양한 딥러닝 기반 서비스 구현 방법을 살펴본다. 개발자들이 직접 딥러닝 기반 데이터 처리, 모델 학습 및 서비스 배포까지 손쉽게 구성할 수 있는 Amazon SageMaker와 Deep Lens를 통해 어떻게 IoT 기반 서비스로 활용할 수 있는지 시연을 통해 알아본다.

1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
@ A
,

2. .
• : : : : N
• : I LP MF D N G
• & A , : :
• , : :
• :

3. . .

4. ) (
•
,
•
•
5 1 &&

5. ( 5 )
• E )
• C -- ( )
- )
• ) )
) )
9 13 % 5

6. -
( 2
• , , 3
P B D
• S I
W M
- ) 1,
0

7. 1) 0 (/
• ,
• '
2 1 5 5

8. 43 , 2 1
• 5
6 : 7 2
•
% %
• 0 1 %
•
5 0 43
) 2 1 (

10. Transforming Industrial Processes with Deep Learning (MAC301),
AWS re:Invent 2016
https://www.youtube.com/watch?v=AHUaor0odh4

11. ArrivalImage
Tower
( )
ArrivalImage
Tower
Departure Image
Tower
ArrivalImage
Tower
Departure Image
Tower

13. -

14. -
•
•
•
Krizhevsky’s CNN
CIFAR CNN
Best Hand-
Engineered
Model

15. -
Original image Activation map Binarymap
2.0
1.0
Google
Net
Conv
Conv
(3*3)
Avg
Pool
3*3
1024 channels

16. :
•
3
S
A
. / . -
. / / .

17. -
2 7
•
•
1 ) (
0 6

18. -
12 -.0 , 2
• ,
•
7
( ) 8
( https://www.amazon.com/b?node=16008589011

19. Active Customers
Up Nearly 5X
Tens of Millions of
Alexa-Enabled Devices

20. ,0 0
+
Alexa Voice
Service
+
5 2
Alexa Skills
Kit

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

22. Deep Learning in Alexa (MAC202), AWS re:Invent 2016
https://www.youtube.com/watch?v=TYRckcVm4WE

23. S A
8 B
2 0 M3
S E
Corpus size
20K+ hours
GPUs - g2.2xlarge
B A G P
U C B S
Distributed SGD

24. 0
100,000
200,000
300,000
400,000
500,000
600,000
0 10 20 30 40 50 60 70 80
Framespersecond
Number of GPU workers
DNN training speed
Strom, Nikko. "Scalable Distributed DNN Training using Commodity GPU Cloud Computing." INTERSPEECH. Vol. 7. 2015.

25. 1
4.75
8.5
12.25
16
1 4.75 8.5 12.25 16
Speedup(x)
# GPUs
Resnet 152
Inceptin V3
Alexnet
Ideal
P2.16xlarge (8 Nvidia Tesla K80 - 16 GPUs)
Synchronous SGD (Stochastic Gradient Descent)
91%
Efficiency
88%
Efficiency
16x P2.16xlarge by AWS CloudFormation
Mounted on Amazon EFS
# GPUs

26. ## train
num_gpus = 4
gpus = [mx.gpu(i) for i in range(num_gpus)]
model = mx.model.FeedForward(
ctx = gpus,
symbol = softmax,
num_round = 20,
learning_rate = 0.01,
momentum = 0.9,
wd = 0.00001)
model.fit(X = train, eval_data = val,
batch_end_callback =
mx.callback.Speedometer(batch_size=batch_size))

27. http://gluon.mxnet.io
-
• ,W NTca I
• ( P C W d MS
K H b
• ) A ) A A
A X
• A ,C C X
NEW!

28. • A Kumar, et al, Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding,
https://arxiv.org/abs/1711.00549
• R Maas, et al, Domain-Specific Utterance End-Point Detection for Speech Recognition - Proc. Interspeech 2017,
http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1673.PDF
• B King et al, Robust Speech Recognition Via Anchor Word Representations - Proc. Interspeech 2017,
http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1570.PDF
• A Kumar et al, Zero-shot learning across heterogeneous overlapping domains - Proc. Interspeech 2017,
http://www.isca-speech.org/archive/Interspeech_2017/pdfs/0516.PDF
• M Sun et al, Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting,
Spoken Language Technology Workshop (SLT), 2016 IEEE
• F Ladhak et al, LatticeRnn: Recurrent Neural Networks Over Lattices - Proc. Interspeech 2016, http://www.isca-
speech.org/archive/Interspeech_2016/pdfs/1583.PDF
• S Panchapagesan et al, Multi-Task Learning and Weighted Cross-Entropy for DNN-Based Keyword Spotting -
Proc. Interspeech 2016, http://www.isca-speech.org/archive/Interspeech_2016/pdfs/1485.PDF
• R Maas et al, Anchored Speech Detection - Proc. Interspeech 2016, http://www.isca-
speech.org/archive/Interspeech_2016/pdfs/1346.PDF
• M Sun et al, Model Shrinking for Embedded Keyword Spotting, 2015 IEEE 14th International Conference on
Machine Learning and Applications (ICMLA)
• N Strom, Scalable distributed DNN training using commodity GPU cloud computing, Annual Conference of the
International Speech Communication Association 2015, http://www.isca-
speech.org/archive/interspeech_2015/papers/i15_1488.pdf

29. NEW!
“Alexa, start the meeting.”
“Alexa, dial 555-8000.”
“Alexa, lower the blinds.”
“Alexa, ask Salesforce which
big deals closed today.”

31. 44.1%
7.7%
3.0%
2.3%
1.0%
1.4%
0.7%
2.2%
0.5%
0.9%
4 ) 0
2 1 % 37
% ( 2 8

32. 2012 2013 2015 20172014 20162008 2009 2010 2011
516
24 48 61 82
159
280
722
1,017
LAUNCHES
1,300+

34. Most robust, fully featured technology infrastructure platform

35. - -
FRAMEWORKS AND INTERFACES
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
VISION
AWS DeepLensAmazon SageMaker
LANGUAGE
Amazon Rekognition Amazon Polly Amazon Lex
Amazon Rekognition Video Amazon Transcribe Amazon Comprehend
Alexa for Business
VR/AR
Amazon Sumerian
APPLICATION SERVICES
Amazon Machine Learning Amazon EMR & SparkMechanical Turk
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
Amazon Translate

36. F R A M E W O R K S A N D I N T E R FA C E S
NVIDIA
Tesla V100 GPUs
P3 1 Petaflop of compute
NVLink 2.0
5,120 Tensor cores
128GB of memory
~14X faster than P2
P3 Instance Deep Learning AMI Frameworks
PLATFORM SERVICES
VISION LANGUAGE VR/IR
APPLICATION SERVICE
AWS DeepLensAmazon SageMaker Amazon Machine Learning Amazon EMR & SparkMechanical Turk
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)

37. 2 0 3
p3.2xlarge
= $5 per hour
p3.2xlarge x 20
= $100 per hour
) ( 1 20

38. Spot Instances (75% ↓)
= $30 per hour

39. 3
$aws ec2-run-instances ami-b232d0db
--instance-count 20
--instance-type p3.2xlarge
--region us-east-1
$aws ec2-stop-instances
i-10a64379 i-10a64280 ...

40. CUSTOMERS RUNNING MACHINE
LEARNING ON AWS TODAY

41. (
)
!

42. H J
.
31
N
31
, - N
31 2
, -
, -
-
NEW!

43. FRAMEWORKS AND INTERFACES
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
VISION
AWS DeepLensAmazon SageMaker
LANGUAGE
Amazon Rekognition Amazon Polly Amazon Lex
Amazon Rekognition Video Amazon Transcribe Amazon Comprehend
Alexa for Business
VR/AR
Amazon Sumerian
APPLICATION SERVICES
Amazon Machine Learning Amazon EMR & SparkMechanical Turk
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
Amazon Translate

44. C A D
,65 .88 387 9 ,41
g g
2 8 a g C
55 ES
2 8 re
t D
J t M
Ip i J
D L J n
2 8 g g
,65 y a 2 8 D
D W L J n
2 +
2
2 2
H D t t A u H
Discrete Classification,
Regression
Linear Learner Supervised
XGBoost Algorithm Supervised
Discrete Recommendations Factorization Machines Supervised
Image Classification Image Classification Algorithm Supervised, CNN
Neural Machine Translation Sequence to Sequence Supervised, seq2seq
Time-series Prediction DeepAR Supervised, RNN
Discrete Groupings K-Means Algorithm Unsupervised
Dimensionality Reduction PCA (Principal Component Analysis) Unsupervised
Topic Determination Latent Dirichlet Allocation (LDA) Unsupervised
Neural Topic Model (NTM) Unsupervised,
Neural Network Based

45. CA
“With Amazon SageMaker, we can accelerate our Artificial Intelligence
initiatives at scale by building and deploying our algorithms on the
platform. We will create novel large-scale machine learning and AI
algorithms and deploy them on this platform to solve complex problems
that can power prosperity for our customers."
- Ashok Srivastava, Chief Data Officer, Intuit

46. Mdt h
z r bg S
Yo
z
2
U k$ nw
c a$ aW w
( e s s
aW p LS
0C K 7 5 B
c 097 4 C m
10
MIN
NEW!
HD video camera
Custom-designed
deep learning
inference engine
Micro-SD
Mini-HDMI
USB
USB
Reset
Audio out
Power
• Intel Atom Processor
• Intel Gen9 graphics
• Ubuntu OS- 16.04 LTS
• 100 GFLOPS performance
• Dual band Wi-Fi
• 8 GB RAM
• 16 GB Storage (eMMC)
• 32 GB SD card
n P ) .
A / C K C 1 ,: 23
• 4 MP camera with MJPEG
• H.264 encoding at 1080p
resolution
• 2 USB ports
• Micro HDMI
• Audio out
• AWS Greengrass
• clDNN Optimized for MXNet

47. FRAMEWORKS AND INTERFACES
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
AWS DeepLensAmazon SageMaker Amazon Machine Learning Amazon EMR & SparkMechanical Turk
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
VISION LANGUAGE
Amazon Rekognition
Image
Amazon
Polly
Amazon
Lex
Amazon Rekognition
Video
Amazon
Transcribe
Amazon
Comprehend
Alexa for
Business
VR/AR
Amazon
Sumerian
APPLICATION SERVICES
Amazon
Translate

48. • L B A M 2
• ,

49. ,
?

50. ,
2
.4
4
3
3
1

51. 43
2, 43
1
.

52. ) 4A
d
) 4A
m
I
f
W
TRg
TRg M
a n o
e ck
i
L
à i lb
TRg P o S
(1 2 352
( 2
( 2
A
( 2
C
(1
( 2
2A
( 2
2 2A
( 2
2 4 3
( 2 2 2

53. AWS ML Customers
APPLICATION SERVICES
Amazon Lex
Amazon Polly
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Rekognition Image
Amazon Rekognition Video
PLATFORM SERVICES
Amazon SageMaker AWS DeepLens
FRAMEWORKS AND INTERFACES
AWS Deep Learning AMI
Apache MXNet
Caffe2
CNTK
PyTorch
TensorFlow
Theano
Torch
Gluon
Keras
AWS ML Platform
DATA LAKE STORAGE
Amazon S3
SECURITY
Access Control
Encryption
COMPUTE
Powerful GPU and CPU Instances
ANALYTICS
Amazon Athena
Amazon Redshift
and Redshift Spectrum
Amazon EMR
(Spark, Hive, Presto, Pig)
AWS Glue
Amazon Kinesis
Amazon QuickSight
Amazon Macie
AWS Organizations
AWS Cloud Platform

54. 1 1 7
• FC S TF ITTQS BWS BNBZP DPN LP NBDI F MFB
• 1FFQ 6FB .7 ITTQS BWS BNBZP DPN LP NBDI F MFB BN S
• 7?8FT ITTQS BWS BNBZP DPN LP NX FT
• F SP 2MPW ITTQS BWS BNBZP DPN LP TF SP GMPW
1 7 017
• . F F T 7BDI F 6FB 7 0P
• ITTQS WWWYPUTUCF DPN QMBYM ST-M ST 96I AQ ZULFX 8D K /CN K U QU
• . F F T 7BDI F 6FB FSS P S
• ITTQS WWWYPUTUCF DPN QMBYM ST-M ST 96I AQ ZULF =0IA QL 8WQI:N
7 1 7 21 1 1
• FC S TF ITTQS WWWB G P T F S DPN
• M FS ITTQ WWWSM FSIB F FT . 2 P T F S Q FSF TBT P S

55. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.