The document provides an overview of Amazon's machine learning stack and services for machine learning. Some key points: 1. Amazon has been investing in machine learning for 20 years and provides a full stack to help customers build, train, deploy and manage machine learning models. 2. The machine learning stack includes frameworks, platforms, application services and infrastructure services. Popular frameworks like TensorFlow, PyTorch and MXNet are supported. 3. Amazon SageMaker is a fully managed service that allows data scientists and developers to build, train and deploy machine learning models easily without having to manage any infrastructure. 4. Other high-level services like Amazon Rekognition provide pre-trained models for tasks like image and video

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김일호
AWS Solutions Architect Manager
Intro to AWS Machine Learning
Solutions

2. Amazon is AI company?

4. Fulfilment
& Logistics
Existing
Products
New
Products
Search &
Discovery
At Amazon, we’ve been making investments
in ML for the last 20 years…

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CUSTOMERS RUNNING MACHINE LEARNING
ON AWS TODAY

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Put machine learning in the hands of every
developer and data scientist
ML @ AWS: Our mission

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AWS Machine Learning Stack

8. The Amazon machine learning stack –
foundation
FRAMEWORKS & INTERFACES
PLATFORM SERVICES
APPLICATION SERVICES

9. Hi AWS,
We have data scientists and R&D for AI
and Machine Learning.

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Fast Deployments
Access highly performant
computing infrastructure in
minutes
Performance
Run your AI/ML workload on the fastest
and most powerful compute instances to
reduce training and simulation times
Scale
Scale out and scale up to
meet all AI/ML workload
requirements.
Cost Efficiency
Pay as you go model and more
efficient training saves cost
Framework Flexibility
Support for frameworks – TensorfFow,
PyTorch, MxNet, Caffe2
Access to Data
High speed connection to data
used for training
C l o u d C o m p u t i n g – T h e b e s t i n f r a s t r u c t u r e c h o i c e f o r A I / M L
w o r k l o a d s

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AI/ML Innovation Enablers
Highly
performant
compute
infrastructure
Availability of
big data
Deep
learning
architectures

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AWS Machine Learning Stack
PLATFORMS
APPLICATION SERVICES
R E K O G N I T I O N R E K O G N I T I O N
V I D E O
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 X
Amazon SageMaker Amazon Mechanical Turk
FRAMEWORKS KERAS
P3
NVIDIA Tesla V100 GPU
accelerated for AI/ML training
Machine Learning
AMIs
INFRASTRUCTURE
&
Greengrass
ML
Amazon Deep Learning AMIs
Compute intensive instances
for AI/ML Inference
C5

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EC2 Compute Instance Types
Well suited for AI/ML
simulation and inference
workloads
Well suited for AI/ML
training workloads

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Amazon EC2 P3 Instances
• Up to eight NVIDIA Tesla V100 GPUs
• 1 PetaFLOP of computational performance
– 14x better than P2
• 300 GB/s GPU-to-GPU communication
(NVLink) – 9X better than P2
• 16GB GPU memory with 900 GB/sec peak
GPU memory bandwidth
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

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B u i l di n g b e tte r m a ch i n e l e a rn i n g m o de l s f o r
re co m m e n da ti o n syste m s
Airbnb is using machine learning to optimize
search recommendations and improve
dynamic pricing guidance for hosts, both of
which translate to increased booking
conversions. With Amazon EC2 P3 instances,
Airbnb has the ability to run training
workloads faster, go through more iterations,
build better machine learning models and
reduce cost.

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Acce l e ra ti n g a u to n o m o u s dri vi n g de ve l o p m e n t o n AWS
Toyota Research Institute accelerates safe
automated driving with deep learning at a global
scale on AWS
“Using Amazon EC2 P3 instances, we reduced
the time to train our models by 75%. This
significantly accelerates our research and
development velocity as we can quickly
incorporate new data and retrain models, explore
ideas, increase model accuracy, and introduce
new features faster,” says Adrien Gaidon, PhD,
Machine Learning Lead, Toyota Research
Institute.

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AWS Deep Learning AMI
• 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

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https://d1.awsstatic.com/whitepapers/nucleus-tensorflow.pdf

19. Hi AWS,
We have data scientists. But, managing
the bottom layer is overhead and hard.

20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS Machine Learning Stack
PLATFORMS
APPLICATION SERVICES
R E K O G N I T I O N R E K O G N I T I O N
V I D E O
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 X
Amazon SageMaker Amazon Mechanical Turk
FRAMEWORKS KERAS
P3
NVIDIA Tesla V100 GPU
accelerated for AI/ML training
Machine Learning
AMIs
INFRASTRUCTURE
&
Greengrass
ML
Amazon Deep Learning AMIs
Compute intensive instances
for AI/ML Inference
C5

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Machine learning process is hard…
Data wrangling
• Setup and manage
Notebook environments
• Get data to
notebooks securely
Experimentation
• Setup and manage
clusters
• Scale/distribute ML
algorithms
Deployment
• Setup and manage
inference clusters
• Manage and auto scale
inference APIs
• Testing, versioning,
and monitoring
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor/
debug/refresh

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…and time consuming
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor/
debug/refresh
6–18
months

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Introducing: Amazon SageMaker
A managed service
that provides the quickest and easiest way for
your data scientists and developers to get
ML models from idea to production.

24. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
End-to-End
Machine Learning
Platform
Zero setup Flexible Model
Training
Pay by the
second
Amazon SageMaker
Build, train, and deploy machine learning models at scale
$

25. Amazon SageMaker
Collect and prepare
training data
Choose and
optimize your ML
algorithm
Set up and manage
environments for
training
Train and tune model
(trial and error)
Deploy model
in production
Scale and manage
the production
environment
E a s i l y b u i l d , t r a i n , a n d d e p l o y m a c h i n e l e a r n i n g m o d e l s

26. Amazon SageMaker
Pre-built
notebooks for
common
problems
K-Means Clustering
Principal Component Analysis
Neural Topic Modelling
Factorization Machines
Linear Learner - Regression
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq
Linear Learner - Classification
ALGORITHMS
Apache MXNet
TensorFlow
Caffe2, CNTK,
PyTorch, Torch
FRAMEWORKS
Set up and m anage
environments for
training
Train and tune
m odel (trial and
error)
Deploy m odel
in production
Scale and m anage the
production environment
Built-in, high
performance
algorithms
BUILD

27. Amazon SageMaker
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN
Deploy model
in production
Scale and manage
the production
environment

28. Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN DEPLOY

29. Amazon SageMaker use case
Intuit is a business and financial software company
that develops and sells financial, accounting and
tax preparation software and related services for
small businesses, accountants and individuals.
“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 at Intuit

30. Amazon SageMaker use case
As the world’s leading provider of high-resolution Earth
imagery, data and analysis, DigitalGlobe works with enormous
amounts of data every day.
“As the world’s leading provider of high-resolution Earth
imagery, data and analysis, DigitalGlobe works with enormous
amounts of data every day. DigitalGlobe is making it easier
for people to find, access, and run compute against our
entire 100PB image library, which is stored in AWS’s cloud, to
apply deep learning to satellite imagery. We plan to use
Amazon SageMaker to train models against petabytes of Earth
observation imagery datasets using hosted Jupyter
notebooks, so DigitalGlobe's Geospatial Big Data Platform
(GBDX) users can just push a button, create a model, and
deploy it all within one scalable distributed environment at
scale.”

31. Hi AWS,
We have everyday developers, no idea
of AI/ML and don’t want to reinvent the
wheel by developing models.

32. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS ML Stack
PLATFORMS
APPLICATION SERVICES
R E K O G N I T I O N R E K O G N I T I O N
V I D E O
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 X
Amazon SageMaker Amazon Mechanical Turk
FRAMEWORKS KERAS
P3
NVIDIA Tesla V100 GPU
accelerated for AI/ML training
Machine Learning
AMIs
INFRASTRUCTURE
&
Greengrass
ML
Amazon Deep Learning AMIs
Compute intensive instances
for AI/ML Inference
C5

33. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Object and Scene
Detection
Facial
Analysis
Face
Comparison
Facial
Recognition
Celebrity
Recognition
Image
Moderation
Amazon Rekognition Image: Deep
learning-based image analysis service

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Results:
| IT’S - 97% |
| MONDAY – 99% |
|but – 97% |keep – 96% |
| Smiling – 99% |
DetectText
Rekognition: Text in Image

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Real-time face recognition against tens of millions of faces
<0.5 second response time
Up to 10M faces
Enable Immediate response
Rekognition: Real-Time Face Search

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How can we apply these powerful
capabilities to video?

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Traditional solutions and limitations
Temporal
information lost
Motion context
lost

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Amazon Rekognition Video
Video Analysis

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Amazon Rekognition Video
One solution for all
Stored Video
Amazon S3
Video Live Stream
Amazon Kinesis Video Stream
Media Search Index
Unsafe Video Detection
Investigative Analysis
Public Safety Immediate Response
Home Monitoring

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Media and Entertainment Public Safety Smart Home
- Search & Filter
- Immediate Response
- Investigative Analysis
- Monitoring
Amazon Rekognition Video
Primary Use Cases

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Amazon confidential
Rekognition Video: Demo

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Amazon Polly
Use Cases
Content Creation
Mobile & Desktop Applications
Internet of Things (IoT)
Education & E-Learning
Telephony
Game Development
Key Features
52 Voices across 25
languages
Lip-Syncing & Text
Highlighting
Fine-grained Voice Control
Custom Vocabularies
Available in 14 AWS regions

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Amazon Transcribe: Automatic Speech
Recognition (Preview)
Time Stamps
Support for both
regular &
telephony audio
Punctuation
& formatting
§
S3 Integration
Recognize
Multiple
Speakers
Custom
Vocabulary

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Call
Centers
Subtitles for
VOD
Transcribe
meetings
Broadcast closed
captions
Amazon Transcribe: Use Cases

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Amazon Lex

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REAL-TIME
TRANSLATION
POWERED BY DEEP
LEARNING
12 LANGUAGE PAIRS
(more to come)
LANGUAGE DETECTION
Amazon Translate: Neural Machine
Translation

47. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon Comprehend: Natural Language
Processing
Sentiment Entities LanguagesKey phrases Topic modeling
Powered By Deep Learning

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A m a z o n . c o m , I n c . i s l o c a t e d i n
S e a t t l e , W A a n d w a s f o u n d e d J u l y
5 t h , 1 9 9 4 b y J e f f B e z o s . O u r
c u s t o m e r s l o v e b u y i n g e v e r y t h i n g
f r o m b o o k s t o b l e n d e r s a t g r e a t
p r i c e s
N a m e d E n t i t i e s
• A m a z o n . c o m : O r g a n i z a t i o n
• S e a t t l e , W A : L o c a t i o n
• J u l y 5 t h , 1 9 9 4 : D a t e
• J e f f B e z o s : P e r s o n
K e y p h r a s e s
• O u r c u s t o m e r s
• b o o k s
• b l e n d e r s
• g r e a t p r i c e s
S e n t i m e n t
• P o s i t i v e
L a n g u a g e
• E n g l i s h
Amazon Comprehend: Extract Insight from
Text

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Multilingual Social Analytics
Twitter
Stream API
Kinesis
Lambda
S3 Athena
Translate Comprehend
Transcribe

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New Solutions in AWS Marketplace
D a t a S o l u t i o n s M L & D a t a S c i e n c e I n t e l l i g e n t S o l u t i o n s
aws.amazon.com/mp/ai

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Potential use cases in enterprise-wise
Media &
Entertainment
Pricing and
Product
Recommendation
Healthcare
& Life
Sciences
Financial
Services
/Trading
Customer
Experience
• Content Commissioning
• Content Creation
• Promotion and Marketing
• Copyright infringement
• Content Auto tagging
• Auto subtitling
• Rights negotiation
• Enhanced customer
service through voice
services and chatbots
• Call center
optimization
• Personal financial
management
• Ecommerce
• Product recommendations
• Credit assessments
• Ad/Search relevance
• Personalization
• Patient health from
clinical data
• Predicting hospital
stay length and re-
admittance
• Drug discovery
• Radiology image
recognition
• Portfolio management/
robo-advising
• Algorithmic trading
• Sentiment/news analysis
• Geospatial image analysis
• Predictive grid computing
capacity management
AI/ML use cases are gaining traction in ALL segments

54. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS AI/ML partner opportunities
MONETIZATION
Expand your global reach
Enable customers
to search for and discover
your solutions
Sell preconfigured, AWS
Marketplace-ready solutions
AWS brand recognition
DEVELOPMENT
Accelerate AI development
Integration with AI Services
and Platforms
Services span from
expert-level to
application developer
SaaS Development Toolkit
CO-SELLING AND GTM
Marketing & co-branding
Funding for PoCs
and innovation
Sales and Solution
Architecture Training
SA & Segment support and
event attendance/speaking

55. Thank you