Speaker: Barnam Bora, Head of AI/ML, APAC, AWS Customer Speaker: Guangda Li, Co-founder & CTO, ViSenze Note: This is part 2 of the deck. WS offers different paths for building and deploying scalable ML solutions. This session provides an insight to how AWS customers are building intelligent systems powered by AI and ML. Learn how these services, in conjunction with the large number of complementary AWS technologies, provide a great platform for our customers to build their own AI and ML powered solutions and drive business value. Towards the latter part of this session, hear how customers are deploying their ML on AWS and can now leverage Marketplace to monetise their models.

1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Building Business Outcomes with
AI & Machine Learning on AWS
Barnam Bora
Head of AI & Machine Learning – Asia-Pacific
Amazon Web Services
Guangda Li
Co-founder & CTO
ViSenze

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Customers need a complete end-to-end Machine Learning stack
M L F R A M E W O R K S &
I N F R A S T R U C T U R E
A I S E R V I C E S
M L S E R V I C E S
A M A Z O N
S A G E M A K E R
G R O U N D T R U T H A L G O R I T H M S
N O T E B O O K S
M A R K E T P L A C E
U N S U P E R V I S E D
L E A R N I N G
S U P E R V I S E D
L E A R N I N G
R E I N F O R C E M E N T
L E A R N I N G
O P T I M I Z A T I O N
( N E O )
T R A I N I N G
H O S T I N G
D E P L O Y M E N T
Frameworks Interfaces Infrastructure
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N
V I D E O
Vision Speech Language Chat-bots Forecasting Recommendations
T E X T R A C T F O R E C A S T P E R S O N A L I Z E
E C 2 P 3
& P 3 D N
E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C
I N F E R E N C E
I N F E R E N T I A

3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Customers need a complete end-to-end Machine Learning stack
M L F R A M E W O R K S &
I N F R A S T R U C T U R E
A I S E R V I C E S
M L S E R V I C E S
A M A Z O N
S A G E M A K E R
G R O U N D T R U T H A L G O R I T H M S
N O T E B O O K S
M A R K E T P L A C E
U N S U P E R V I S E D
L E A R N I N G
S U P E R V I S E D
L E A R N I N G
R E I N F O R C E M E N T
L E A R N I N G
O P T I M I Z A T I O N
( N E O )
T R A I N I N G
H O S T I N G
D E P L O Y M E N T
Frameworks Interfaces Infrastructure
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N
V I D E O
Vision Speech Language Chat-bots Forecasting Recommendations
T E X T R A C T F O R E C A S T P E R S O N A L I Z E
E C 2 P 3
& P 3 D N
E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C
I N F E R E N C E
I N F E R E N T I A

4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
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
AI
Services
AWS
Batch
AWS IoT
AWS
Greengrass
Lambda
function
Ingest Store Compute Database Analyze Intelligence The Edge
ML is only a component of a overall complete solution.
A W S
D E E P L E N S
and Many more…

5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Our Customers are actively Building
innovative ML based Intelligent systems
to empower their businesses, at a global
scale through AWS

6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Deployment time down
by 90% with Amazon
SageMaker
< 1 W E E K6 M O N T H S
BUILDERS can BUILD Intelligent Systems much faster with AWS….

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“소니는 Greengrass를 활용해서 공장 기계에 장착된 센서 Data를 모아 각 장비의 실시간 상태를 파악하고
Machine Learning을 활용하여 Predictive Maintenance에 활용하기 시작했다.” -Ryuji Takehara, Cloud System Architect, Sony
• Predictive Maintenance à Bearing
기기노후화로 인한 Performance 저하
파악
• 향후, Factory Solution외 타 IoT
솔루션으로 확장할 계획
• AWS Cloud 환경 내 ML Model Training
• Model을 High Frequency 데이터 처리용
보드에 Greengrass로 내려 Inference 수행
• Greengrass를 통해 Model의 지속적인
개선 및 Deploy를 쉽게 할 수 있음
• 공장 내 다양한 제조 장비가 존재하여
기기別 Customize된 ML Model 개발
중à수백 개의 Model 개발/관리에
어려움
• 가속 센서 기반 Anomaly Detection 위해
High Frequency(10KHz↑) 센서데이터 필요
9
Greengrass 기반 Edge Computing
à‘Anomaly Detection’ Factory Use Case 구현

10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
10
“과거의 매뉴얼 Inspection은 30분동안 겨우 50개의 Chip을 검사할 수 있었고,
타 장비와 연계되어 있지 않아 통합적인 분석이 어려웠다＂ – CTO of Nanotronics
Wafer Level Inspection에
ML 적용을 검토해 볼 수 있음
• AI/ML 기반 Optical Inspection 장비로 Defect를 자동으로 발견, 수 분 내에 10만개의 Chip을
검사하여 Time-to-Market을 제고
Optical Inspection 장비 AI/ML 기반 Defect Analyzer
• 공정 전반에 배치되어 있는 타 Optical Inspection 장비와 연계하여 통합적인 Defect 분석이 가능함
ü 자체 보유한 ML기반 Display Panel
Inspection S/W를 클라우드
환경에서 Training 하는 PoC를 검토
중임

11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Tensorflow
AWS
Rekognition
Amazon
Polly
Safety Helmet
Recognition
Facial
Recognition
Voice
Message
+
“David,
Please
wear a
helmet!”
Recognition Alerts
Side view Front view

12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
More ML happens on AWS than anywhere else

13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Let’s hear from…

14. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

15. Visual Search and Recognition at ViSenze
Guangda LI
CTO and Co-founder
www.visenze.com

16. • Started as Spin off from NExT(NUS-Tsinghua Research centre); Raised 34M USD funding from VCs
• Based on computer vision and deep learning research, ViSenze provides visual search and image
recognition solution for some of the largest companies in the world
• Customers include the top fast fashion retailer, sports brand and mobile phone manufacturer
ViSenze has been recognized amongst:
• Top 5 deep learning companies - VentureBeat 2017
• Top AI Product AI (Retail) – CogX London 2017
• Top 40 global Breakthrough Brands - InterBrand 2017
About ViSenze
Top 25 AI companies
in 2018

17. Under the Hood
• Built by Computer Vision scientists and software development
experts (40+ R&D in CV, ML, Infra)
• 4 patent applications (granted and pending) in various stages
• Global scale distributed architecture supporting over 1 Billion
• Low latency and high-throughput architect design
• In-house distributed GPU training platform and tool development
• Independent validations: ImageNet, client evaluations
Deep Learning & Computer Vision AI DevOps Platform
Continuous Training Domain Models
ViSenze singled out in keynote speech by
Prof Li Fei Fei of Stanford at CVPR 2017
ViSenze partners Nvidia using latest GPUs for
image processing and CNN model training

18. The Rise of Visual Content on Internet
Gen Z prefers to communicate with Images Image Growth - More than 3B photos per day

19. Major players are already driving this shift of visual search adoption
250M visual searches/month - Pinterest, May 2017
360M visual searches/month - Taobao, July 2017
https://www.forbes.com/sites/kathleenchaykowski/2017/02/08/pinterest-debuts-new-camera-lens-search-tools-to-find-real-world-objects-online/#3223bbc060e1
Amazon Visual Search Pinterest Lens Samsung Bixby Vision Google Lens

20. H&M, ASOS and UNIQLO Visual Search powered by ViSenze
Image Search is now a common feature on leading retailer apps

21. How It Works : Product recommendation

22. Improving User Engagement Through Artificial Intelligence
A.I. powered visual search and recognition
solutions improve engagement and
conversions
30% 50% 5x 160%
higher conversions
on image search
over text based
search
higher CTR of
shoppers who click
on visually similar
products
higher conversion
rates for shoppers
clicking on visually
similar products
increase in
engagement for
shoppers who used
find similar

23. Platform Solution: ViSenze Shopping Lens on Smartphones
200M+ Products from over 800+ major global retailer on ViSenze global affiliate network

24. Query image
v[1:4096]
Image
Database
v1
v2
…
vN
Features
(AlexNet)
Similarity + kNN
(Cosine similarity)
Visual Search 1-2-3

25. Query
time
Index
time
Offline training
shoe
Detection
model
Search
Index
Objects
Embedding
models
Extract
features
Reference images
Compression
codebook
Hash
model
Extract
features
NN search
and Re-
ranking
Ranked results
Visual Search in ViSenze
Deep
learning
based
training

26. Tagging Solutions and Use Cases
Automated Tagging Catalogue Management
Tag entire image libraries based on visual attributes for better
search results
Image Filtering and Moderation
Tag entire image libraries based on visual attributes
Improve discoverability and conversions
Fashion Attributes
Style Attributes
Image Quality

27. Tagging Solutions and Use Cases - Example
Fashion Attribute
Fashion style
& Occasion

28. Fine-grained Fashion Attribute Recognition
25 types of neckline:
zip_neck
henley
off_shoulder
scoop
crew_neck
round_neck
v_neck
cowl
turtleneck
...
peter_pan
keyhole
one_shoulder
tie_up_neck
other
60 types of product categories:
sweater
sweatshirt_hoodies
blouse
shirt
t_shirt
polo_shirt
top
camisole
tank_top
…
clutch_purse
card_holder
pouch
wallet
others
14 types of product pattern:
stripes
text
animal_print
big_graphic
...
plaid
solid
other

29. Life of A ML pipeline: Continuous Data Cleaning
Data Management Challenges in Production Machine Learning, Alkis Polyzotis etc 2017, SIGMOD

30. The Gap between DL Software and Production DL System
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform D. Baylor etc KDD, 2017.
Deep learning framework is a
small component for production
level deep learning development

31. End-end Deep Learning Platform to Accelerate the Model Development

32. Hidden Facts for Production Deep learning Development
Key winning formula for DL-based applications
• Transfer domain Expertise into clear requirement
• Large scale high quality training data and data management
• Intensive and faster feedback
Continuous performance improvement
• Hard to result from methodology improvement
• Usually results from data driven improvement

33. DNN Inference Engine on AWS
● Caffe/MXNet/Caffe2 model support
● Batch inference
● TensorRT for Nvidia GPU/OpenVINO for Intel CPU
● Deployment on AWS P2/C series
● scale to 4000+ CPUs, with peak throughput 5M
images/hour.Based on the price, choose the most
cost-efficient instance type.

34. Deployment of Models to AWS Marketplace

35. Visual Search and Recognition at ViSenze
Guangda LI
guangda@visenze.com
www.visenze.com

36. Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Barnam Bora & Guangda Li