熱門創新服務專題 Unlocking New Todays: Artificial Intelligence and Data Platforms on AWS (Level 200) Speaker: Paul Yung, Head of Territory Development HKT, AWS

1. Unlocking New Todays
Artificial Intelligence and Data Platforms on AWS
Paul Yung - Head of Territory Development, HKT
pyung@amazon.com
Track 4
15:05pm - 15:45pm
Level: 200

2. Thank You

5. “AI Technologies Will Be in Almost Every New
Software Product by 2020” – Gartner

6. Deep
Learning
(DL)
Machine
Learning
(ML)
Artificial
Intelligence
(AI)
What is AI?
A system or service which can
perform tasks that usually require
human intelligence

7. Product
recommendation
engine
Robot-enabled
fulfillment
centers
New
product
categories
At Amazon, we’ve been making investments
in ML /DL for the last 22+ years and we share
our knowledge and capabilities with our
customers
20181995
ML-driven supply
chain and
capacity planning
Checkout-free
shopping
using deep learning
Our deep experience differentiates our services

8. To be Earth’s most customer-centric
company
We concentrate first on the success of customer,
then our own success

9. Customer Running ML on AWS Today

10. Scientific breakthrough
The Promise of Artificial Intelligence: INNOVATION
Autonomous machines Seamless experience

11. Stanford University- Improving Health Outcomes
• Early detection of diabetic complications
• Leading cause of blindness in adults
• Catch it early enough, prevented 90% of
time (糖尿病性視網膜病變)
“Before AWS, we couldn’t
even attempt these
projects….AWS makes
research liberating.”
Jason Su
Stanford University Student

12. Autonomous Driving Systems

13. Systems see which items have
been taken or returned
Machine learning understands
in-store and purchase patterns
Combination of computer vision,
sensor fusion, and deep learning
Just Walk Out Technology

14. Personalized content
- Account access
- Track spending
- Check balances
- Pay bills
- Prevent fraud
Voice recognition
understands your requests
Natural speech responses
create conversation

16. AI Computer Vision Implementation to
identify empty space and detect
object misplacements

17. PrimeAir
Edge compute allows for real-time
decisions to be made in-vehicle, driven by
computer vision and deep learning
Sense and Avoid Technology

18. The Challenge For Artificial Intelligence: SCALE
Data

19. Data Training
The Challenge For Artificial Intelligence: SCALE

20. Data Training Prediction
The Challenge For Artificial Intelligence: SCALE

21. “The future is here,
it’s just not evenly distributed yet”
William Gibson
Author of Neuromancer (神經漫遊)

22. We want to democratize AI
We want to lower the costs and barriers to
Machine Learning
We want to put machine learning in the hands
of every developer and data scientist

23. Aggressive migration
New data created on AWS
Data
Training Prediction
PBs of existing data
The Challenge For Artificial Intelligence: SCALE

24. Tons of GPUs
Elastic capacity
Training
Prediction
Pre-built images
The Challenge For Artificial Intelligence: SCALE
Aggressive migration
New data created on AWS
Data
PBs of existing data

25. Tons of GPUs and CPUs
Serverless
At the Edge, On IoT Devices
Prediction
The Challenge For Artificial Intelligence: SCALE
Tons of GPUs
Elastic capacity
Training
Pre-built images
Aggressive migration
New data created on AWS
Data
PBs of existing data

26. Frameworks &
Infrastructure
AWS Deep Learning AMI
GPU
(P3 Instances)
MobileCPU IoT (Greengrass)
Vision:
Rekognition Image
Rekognition Video
Speech:
Polly
Transcribe
Language:
Lex Translate
Comprehend
Apache
MXNet
PyTorchCNTK Keras
Caffe2
& Caffe
TensorFlow Gluon
AWS ML | A Full Stack
Application
Services
Platform
Services
Amazon Machine
Learning
Mechanical
Turk
Spark &
EMR
Amazon
SageMaker
AWS
DeepLens
Amazon confidential

27. Frameworks &
Infrastructure
Vision:
Rekognition Image
Rekognition Video
Speech:
Polly
Transcribe
Language:
Lex Translate
Comprehend
AWS ML | A Full Stack
Application
Services
Platform
Services
Amazon confidential

28. Image Recognition and Analysis powered by
Deep Learning allows search, verification, and
organization millions of images
Potential Use Cases
Searchable Image Library Detect Inappropriate Content in Images
Face-based User Verification Sentiment Analysis
Facial Recognition Celebrity Identification
Vision Service - Amazon Rekognition Image

29. Rekognition Image: Object & Scene Detection
Object and scene detection makes it easy for you to add
features that search, filter, and curate large image libraries
Identify objects and scenes and provide confidence scores
Flower
Arrangement
Chair
Coffee Table
Living Room Indoors
Furniture
Cushion
Vase
Maple
Villa
Plant
Garden
Water
Swimming Pool
Tree
Potted Plant
Backyard
Patio

30. Demographic Data
Facial Landmarks
Sentiment Expressed
Image Quality
Brightness: 25.84
Sharpness: 160
General Attributes
Rekognition Image: Facial Detection

31. Measure the likelihood that faces are of the same person
Similarity 93% Similarity 0%
Rekognition Image: Facial Comparison

32. Rekognition Image: Facial Search
Find similar faces in a large collection of images

33. Amazon Rekognition Video
Video Analysis
Object & Activity
Detection
Person
Tracking
Face
Recognition
Real-time Live
Stream
Content
Moderation
• Fully managed, scalable, and easy-to-use video analysis service
• Deep learning-based -> Continuously improving
• Integrated with Amazon S3, Amazon Kinesis Video Streams, AWS
Lambda – get started with video analysis right out-of-the-box

34. Turn text into lifelike speech using
deep learning technologies to
synthesize speech that sounds like a
human voice
Potential Use Cases
Content Creation Education & E-learning
Mobile & Desktop Applications Customer Contact Center
Internet of Things (IoT) Accessibility
Speech Service - Amazon Polly
Price $4.00 per 1 million characters for speech requests

35. Let’s take a listen…
「To be or not to be」莎士比亞齣戲《哈姆雷特》第三幕第一場

36. “Today in Taipei, it’s > 16°F”
‘"We live for the music" live from the Madison Square Garden.’
1. Automatic, Accurate Text Processing
Polly: A Focus On Voice Quality & Pronunciation

37. Polly: A Focus On Voice Quality & Pronunciation
“She sells sea shells by the sea shore”
1. Automatic, Accurate Text Processing
2. Intelligible and Easy to Understand

38. 1. Automatic, Accurate Text Processing
2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
Polly: A Focus On Voice Quality & Pronunciation
“Richard’s number is 2122341237“
“Richard’s number is 2122341237“
Telephone Number

39. 1. Automatic, Accurate Text Processing
2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
4. Customized Pronunciation
Polly: A Focus On Voice Quality & Pronunciation
“My daughter’s name is Kaja.”
“My daughter’s name is Kaja.”

40. FM Wakayama is using Amazon Polly as an AI Announcer
FM Wakayama is a public radio station that
broadcasts out of Wakayama, Japan.
Listeners seemed to think
the news were read out by
a real person
Akimasa Yamaguchi
Station Manager, FM Wakayama
”
“ • Amazon Polly is used for late-
night and early-morning radio
broadcasts.
• Amazon Polly voices can be
leveraged for Public Service
Announcements during times of
emergency, in many languages
• “In the future, Amazon Polly may
save someone’s life.”

41. Amazon Transcribe (Preview)
Automatic conversion of speech into accurate,
grammatically correct text
Support for
telephony
audio
Timestamp
generation
Intelligent
punctuation and
formatting
Recognize
multiple
speakers
Custom
vocabulary
Multiple
languages
e .g. Episode of "Game of Thrones” (63 mins), cost @ USD 1.512

42. Amazon Translate (Preview)
Natural and fluent language translation
Real-time
translation
Batch
analysis
Automatic
language
recognition
Low cost
Price @$ 1 5 pe r m il l ion charact e rs

45. BUT WHAT DOES ALL THIS TEXT MEAN?

46. Amazon Comprehend
Discover insights and relationships in text
Entities
Key Phrases
Language
Sentiment
Amazon
Comprehend
Social media posts, emails,
web pages, documents,
phone transcriptions
Input Output

53. STORM
WORLD SERIES
STOCK MARKET
WASHINGTON
LI BRARY OF
NEWS ARTICLES
Amazon
Comprehend
Discover insights and relationships in text
Amazon Comprehend
e . g . 3 0 0 d o c u m e n t @ 1 M B e a c h
C o m p r e h e n d - 4 5 m i n s t o c o m p l e t e @ U S D 1 . 8

54. Frameworks &
Infrastructure
AWS Deep Learning AMI
Apache
MXNet
PyTorchCNTK Keras
Caffe2
& Caffe
TensorFlow Gluon
AWS ML | A Full Stack
Application
Services
Platform
Services
Amazon confidential

55. We want to provide Freedom and choice

56. Support for all major frameworks
AWS Deep Learning AMI
Apache
MXNet
Torch
CNTKKeras
Theano
Caffe2
& Caffe
TensorFlow
Amazon EC2
AnacondaIntel MKL
Nvidia CUDA &
cuDNN
Python2 &
Python3
• Get started quickly with easy-to-launch tutorials
• Hassle-free setup and configuration
• Pay only for what you use – no additional charge
for the AMI
• Accelerate your model training and deployment
• Support for popular deep learning frameworks

57. Frameworks &
Infrastructure
GPU
(P3 Instances)
MobileCPU IoT (Greengrass)
AWS ML | A Full Stack
Application
Services
Platform
Services
Amazon confidential

58. Train model in the cloud Run model at the edge
AWS
Greengrass
AWS IoTAmazon
EC2

59. Amazon EC2 P3 Instances
• Up to 8 x NVIDIA Tesla V100 GPUs (Latest
generation)
• 5,120 Tensor Cores., 128GB GPU memory
• 1 PetaFLOP of computational performance –
14x faster than P2
• NVLink 2.0, 300 GB/s GPU-to-GPU
communication – 9X better than P2
T h e f a s t e s t , m o s t p o w e r f u l G P U i n s t a n c e s i n t h e c l o u d

60. Frameworks &
Infrastructure
AWS ML | A Full Stack
Application
Services
Platform
Services
Amazon Machine
Learning
Mechanical
Turk
Spark &
EMR
Amazon
SageMaker
AWS
DeepLens
Amazon confidential

61. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Model Selection
Regression
Classification
Clustering
Data Prep
Data Cleansing
(Missing Values,
Normalization, etc..)
Model Training Model Evaluation Model DeploymentData SelectionData Selection
Video
Databases
(Join, Filter, etc..)
Dim Reduce
(ID Salient Features)
Annotation
(Label the ground
truth)
Connectionists
(Neural Networks)
Symbolists
(Logic)
Analogists
(SVMs)
Bayesians
(Graphical Networks)
Evolutionists
(Genetic Agents)
Boosters
(Boosted Trees)
HPO
(Non-learned
parameter tuning)
Cross Validation
(Generalization?)
Metrics
(Bias, Variance, F1
Score)
Regularization
(Dropout, L2, etc..)
Portability
Ensembling
New Predictions
ML is still too complicated for everyday developers

62. Solves All These Problems !
Introducing: Amazon SageMaker

63. Supercharge your Machine Learning Solutions
with Amazon SageMaker
Jhen-Wei Huang
Solution Architect
Track 4
16:00pm - 16:40pm
Level: 300

64. Media &
Entertainment
Pricing and
Product
Recommendation
Healthcare
& Life
Sciences
Financial
Services
/Trading
Customer
Experience
AI/ML use cases are gaining traction in ALL segments
The potential impact of AI/ML is enterprise-wide
Unlock new todays

65. End
Thank You !
Paul Yung
pyung@amazon.com