1) AWS provides a variety of machine learning services for building, training, and deploying machine learning models at scale including Amazon SageMaker, Amazon Rekognition, and Amazon Lex. 2) AWS offers powerful GPU instance types like P3 for accelerating deep learning workloads and training complex models faster. 3) Customers can use AWS GPU instances and services for a range of applications from image and video analysis to natural language processing.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
CA . E A 1A E A C8 E 8E
A 1 CG 8 , B
, A E 2
1 M a ST NK P
WJUY I 03

2. yr cVsf
3J EGA I GSNE M
)JCIENJ J EIKJEIN
W w aVd
b l k
)GJO ,LJIN
n V
) JONA & ELA N )JIIA N
3 )GJO L EG
)GJO . 3
)JIBEC
ypi
)GJO N D
LOMNA PEMJL
mw
G MNE (A IMN GF
)GJO ,JLH NEJI
5KM JLFM
iV
u f
3 I CAHAIN
)JIMJGA )2
v M
P .
SNDJI O S
Vr u
Ve x s l hV e aVd
( 01 2
b tzVn
) ONJ GEIC 2 H
G MNE 2J ( G I EIC
) )JIN EIAL ALPE A
iVw g w
JLF K AM JLF J M
JLF3 EG
fn Ve
( G EAL ,
NJL CA NAQ S
mVixVf
SI HJ (
A MDEBN G MNE) DA
G MNE 3 K A O A
EIAMEM NDAI N
EKAGEIA
w aVd cVsf
KK NLA H G MNE MA L D
,
m n
ELA NJLS
ALPE A
bVo
)J A AKGJS
)J A)JHHEN
)J A EKAGEIA
) 8
H TJI 2AR 3 DEIA
2A LIEIC JGGS
AFJCIENEJI
,https://aws.amazon.com/jp/products/

3. * ,8 6S a
( A S A 8 C AS
) , +02 01 M 8 W 6a
a h
S6 hf (
cf
d
a
a a a a
A 8
aA 6 6 aS
W
h
6 6 a
54 A
W
G
) , +02 01
New
*4 20 3 1 )
https://aws.amazon.com/jp/about-aws/global-infrastructure/

4. 41 -
( )
https://aws.amazon.com/jp/compliance/pci-data-privacy-protection-hipaa-soc-fedramp-faqs/

5. W*
S A ( ) A

6. 1 S * 1 S
A ) W 0(
A ) 1 10(
7

7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

8. 1
5

10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

12. W TS
• X
• X
•
•
• ce
•
13
• A
•
•
• h
• X
• ce
• X
•
•

13. 14

14. Batch / Real
time
15

15. 20
Batch / Real
time
16

16. Batch / Real
time
AWS
AWS ETL
17

17. Batch / Real
time
AWS
AWS ETL
Amazon Mechanical TurkAWS ML
18

18. Batch / Real
time
19
AI/ML/DL

19. Batch / Real
time
20

20. Batch / Real
time
21

21. Batch / Real
time
AWS
AWS ETL
Amazon Mechanical TurkAWS ML
AWS
22

22. 23
Batch / Real
time
AWS
AWS ETL
Amazon Mechanical TurkAWS ML
AWS

23. Amazon
Kinesis
AWS Glue
Amazon
S3
Amazon
Glacier
Amazon ECS
Amazon
EC2
Lambda
function
Amazon
DynamoDB
Amazon
RDS
Amazon
Redshift
Amazon
Athena
Amazon ES
Amazon
EMR
Amazon
QuickSight
Amazon Kinesis
Analytics
Amazon Kinesis
Streams
Amazon
SageMaker
Amazon Machine
Learning
AWS Batch
AWS IoT
AWS
Greengrass
Lambda
function
Ingest Store Compute Database Analyze Intelligence The Edge
AWS

24. 3F
RG W
A B
RG
S LI
B 3
)3B D3E
M Q
C ( B
) C
RG
IGK T
) 3
B 3
RG
2
C B
RG
A A
25

25. Redshift
EMR
(Yarn/Spark)
Aggregator
Model
Development
&Training
/
Amazon
Athena
Realtime Event Processor
Agg.
Cached
Data
Aggregator
Realtime
Search
Kinesis
Analytics
Decision
on the
Cloud
Feedback
Aggregator
Quicksight
Kinesis
Streams
Kinesis Firehose
Amazon S3
AWS
Greengrass
Lambda
function
AWS Glue
AWS IOT
ML Service

26. 27
https://robotstart.info/2018/03/27/toyota-aws.html

27. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

28. Batch / Real
time
AWS ML
29

29. Services
Amazon
Rekognition
Image
Platform
Engines
TensorFlow Caffe Torch Theano CNTK Keras
CPU IoT MobileInfrastructure GPU
MXNet
AWS Deep Learning AMI (Amazon Linux / Ubuntu / Windows)
Amazon
Polly
Amazon
Lex
Amazon
Comprehend
Amazon
Translate
Amazon
Transcribe
Amazon
Rekognition
Video
Amazon
Machine
Learning
Apache Spark
Amazon EMR
Amazon
Kinesis
AWS Batch Amazon ECS
Amazon
SageMaker
30

30. Services
Amazon
Rekognition
Image
Platform
Engines
TensorFlow Caffe Torch Theano CNTK Keras
CPU IoT MobileInfrastructure GPU
MXNet
AWS Deep Learning AMI (Amazon Linux / Ubuntu / Windows)
Amazon
Polly
Amazon
Lex
Amazon
Comprehend
Amazon
Translate
Amazon
Transcribe
Amazon
Rekognition
Video
Amazon
Machine
Learning
Apache Spark
Amazon EMR
Amazon
Kinesis
AWS Batch Amazon ECS
Amazon
SageMaker
31

31. 8
2 563
1 0 72
1 1
1 1 7 2 11 0 72
2017/10/26

32. N U UG UG
• 0 1- A 3 a
- 0 N I D
• V Ad 3 N U P T
AV l e
• U b e
NVIDIA Roadmap
GTC 2017
33
https://aws.amazon.com/jp/ec2/instance-types/p3/

33. 34

34. (8 A: ) 1 >
03 ( 12 DCE GI
• 1 3: >
1 > DCE GI
1 > 3 3: > DCE GI
0
20
40
60
80
100
120
140
K80 P100 V100
Mixed/FP16 Perf (TFLOPS)
0
2
4
6
8
10
12
14
16
K80 P100 V100
FP32 Perf (TFLOPS)
0
1
2
3
4
5
6
7
8
K80 P100 V100
FP64 Perf (TFLOPS)
0
1000
2000
3000
4000
5000
6000
K80 P100 V100
Resnet-50 8 GPU
(Images/sec)
1 0
2 .

35. https://speakerdeck.com/ayemos/powering-by-aws-gpu-instances-in-cookpad-inc

36. https://speakerdeck.com/rtechkouhou/hua-xiang-chu-li-ji-jie-xue-xi-hefalsep3huo-yong

37. 3
38

38. 39 https://aws.amazon.com/machine-learning/amis/

39. 40
CUDA9, nvidia-docker AMI
nvidia-docker
NVIDIA GPU Cloud Docker
Docker
https://ngc.nvidia.com
https://aws.amazon.com/marketplace/pp/B076K31M1S
http://docs.nvidia.com/ngc/ngc-aws-setup-guide/index.html

40. C
5
Instance Name vCPUs RAM EBS Bandwidth
Network
Bandwidth
c5.large 2 4 GiB Up to 2.25 Gbps Up to 10 Gbps
c5.xlarge 4 8 GiB Up to 2.25 Gbps Up to 10 Gbps
c5.2xlarge 8 16 GiB Up to 2.25 Gbps Up to 10 Gbps
c5.4xlarge 16 32 GiB 2.25 Gbps Up to 10 Gbps
c5.9xlarge 36 72 GiB 4.5 Gbps 10 Gbps
c5.18xlarge 72 144 GiB 9 Gbps 25 Gbps
41
https://aws.amazon.com/jp/ec2/instance-types/c5/

41. /
42
• FM I S L N
X
• G
U A
• N cS
P a W
• X
Trained models
and Lambdas
Extracted
IntelligenceInferences and
take actions
locally on device
AWS Cloud
for training
PREVIEW
AVAILABLE
https://aws.amazon.com/jp/greengrass/ml/

42. 43
TuSimple’s deep learning requirements for training
versus implementation are very different. The
models are created and trained in a multiple-GPU-
based Amazon Web Services cloud environment.
https://www.oreilly.com/ideas/self-driving-trucks-enter-the-fast-lane-using-deep-learning

43. 228 2
• e WL L
e S
• 22 1 nw P GM p
f eT AN S s
• ,L FG e o t
r S l D C
• e W g F
2 1 2 0 2 Xa
44 https://aws.amazon.com/jp/deeplens/

44. • O O
D S e
• A O
•
W
45