Drive Digital Transformation Using AI

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Drive Digital Transformation
Using AI

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Centerpiece for digital transformation
Customer
experience
Business
operations
Decision
making
Innovation Competitive
advantage
of digital transformation initiatives
supported by AI in 201940%

Our mission at AWS
Put machine learning in the hands
of every developer

Our unique
approach
Customer-focused
90%+ of our ML roadmap
is defined by customers
Breadth & depth
More AI and ML services
in production than any
other provider
Embedded R&D
Customer-centric approach to
advancing the state of the art
Security & analytics
Deepest set of security and
encryption features, with robust
analytics capabilities
Multi-framework
Support for the most
popular frameworks
Pace of innovation
200+ new ML launches
in the last year

Customers have used
machine learning on AWS
10,000+
AWS holds the top spots on DAWN,
Stanford’s deep learning benchmark, for
fastest training time, lowest cost, and lowest
inference latency
of TensorFlow projects in the cloud
run on AWS85%
of deep learning in the cloud runs on
AWS81%

More machine learning happens on AWS than anywhere else

Culture
Technology

Bringing AI into your
digital transformation
requires a new “stack”
that makes it easier to
put ML to work
Technology

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
The Amazon ML stack: broadest & deepest set of capabilities
A I S E R V I C E S
Vision | Documents | Speech | Language | Chatbots | Forecasting | Recommendations
M L S E R V I C E S Data labeling | Pre-built algorithms & notebooks | One-click training and deployment
Build, train, and deploy machine learning models fast
Easily add intelligence to applications without machine
learning skills
Flexibility & choice, highest-performing infrastructure
Support for ML frameworks | Compute options purpose-built for ML

The Amazon ML stack: Broadest & deepest set of capabilities
A I S E R V I C E S
A M A Z O N
R E K O G N I T I O N
I M A G E
A M A Z O N
P O L L Y
A M A Z O N
T R A N S C R I B E
A M A Z O N
T R A N S L A T E
A M A Z O N
C O M P R E H E N D
& C O M P R E H E N D
M E D I C A L
A M A Z O N
L E X
V I D E O
Vision Speech Chatbots
A M A Z O N
S A G E M A K E R
B U I L D T R A I N
A M A Z O N
F O R E C A S T
A M A Z O N
T E X T R A C T
A M A Z O N
P E R S O N A L I Z E
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization (N E O )
One-click deployment & hosting
M L S E R V I C E S
F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e
A M A Z O N E L A S T I C
C O M P U T E C L O U D
( A M A Z O N E C 2 ) P 3
& P 3 d n
E C 2 C 5 F P G A s A W S I o T
G R E E N G R A S S
A M A Z O N
E L A S T I C
I N F E R E N C E
Reinforcement learning
Algorithms & models ( A W S M A R K E T P L A C E
F O R M A C H I N E L E A R N I N G )
Language Forecasting Recommendations

Put AI to work for your business
Pre-trained AI services that require
no ML skills or training
Easily add intelligence to your
existing apps and workflows
Quality and accuracy from
continuously-learning APIs
A I S E R V I C E S
Vision Speech ChatbotsLanguage Forecasting Recommendations
A M A Z O N
I M A G E
A M A Z O N
P O L L Y
A M A Z O N
T R A N S C R I B E
A M A Z O N
T R A N S L A T E
A M A Z O N
C O M P R E H E N D
M E D I C A L
A M A Z O N
L E X
V I D E O
A M A Z O N
F O R E C A S T
A M A Z O N
T E X T R A C T
A M A Z O N

Modernize your contact center to improve customer service
Conversational chat bots | call transcription | intelligent routing | sentiment analysis | VoC analytics text-to
speech | multilingual omni-channel communication
A M A Z O N
T R A N S L A T E
A M A Z O N
C O M P R E H E N D
A M A Z O N
L E X
A M A Z O N
P O L L Y
A M A Z O N
T R A N S C R I B E
A M A Z O N
P O L L Y
A M A Z O N
T R A N S C R I B E

Meaningful
customer interactions
Vonage uses Amazon Lex and Amazon Polly
to develop natural language driven,
conversational apps to respond to customer
requests with real-time and contextual
intelligence, improving response time, and
quality of service.

Use AI services to strengthen safety & security
accurate facial analysis | identity protection | metadata extraction
A M A Z O N
C O M P R E H E N D &
A M A Z O N
C O M P R E H E N D
M E D I C A L
A M A Z O N
I M A G E
A M A Z O N
V I D E O

Quick demo

Real-time
identity verification
Aella Credit uses Amazon Rekognition to
analyze images to verify an individual’s identity in
real time without human intervention, allowing it
to provide instant loans to eligible customers
through its mobile app.

Automate media workflows to reduce costs & monetize content
Content moderation | contextual ad insertion | searchable media library
custom facial recognition | multi-language metadata search
A M A Z O N
C O M P R E H E N D
R A M A Z O N
E K O G N I T I O N
I M A G E
A M A Z O N
V I D E O
A M A Z O N
T R A N S C R I B E
A M A Z O N
T R A N S L A T E
A M A Z O N
T E X T R A C T

Scaling video indexing
C-SPAN uses Amazon Rekognition to
automatically index video news footage
for search. With Amazon Rekognition, C-SPAN
reduced indexing time per video from one
hour to 20 minutes and uploaded 97,000
images in under two hours.

Reduce localization costs & improve accuracy
Custom vocabulary | timestamp generation | secure real-time translation | language identification
A M A Z O N
P O L L Y
A M A Z O N
T R A N S C R I B E
A M A Z O N
T R A N S L A T E
A M A Z O N
C O M P R E H E N D

Scaling
real-time translation
Using Amazon Translate, Lionbridge is able to
scale machine translation in order to localize
content faster and in more languages. Using
Amazon Translate, Lionbridge was able to reduce
translation costs by 20%.

Understand the voice of your customer
Sentiment analysis | app localization | translation services | transcription services | cataloging media | accessibility
A M A Z O N
T R A N S C R I B E
A M A Z O N
T R A N S L A T E
A M A Z O N
C O M P R E H E N D
A M A Z O N
I M A G E
A M A Z O N
V I D E O

Targeted
customer acquisition
VidMob uses Amazon Rekognition and
Amazon Transcribe for metadata extraction
and sentiment analysis, to help marketers
understand which videos resonate with audiences.
This allows marketers to promote targeted
content to acquire new customers.

Personalize customer experiences with targeted recommendations
Recommendation technology used by Amazon.com | context-aware recommendations sentiment
analysis | VoC analytics
A M A Z O N
A M A Z O N
C O M P R E H E N D
A M A Z O N
I M A G E
A M A Z O N
V I D E O
NEW

Personalizing
customer experiences
Domino’s uses Amazon Personalize to customize
and scale relevant marketing communications to
customers based on time, context, and content,
thereby improving and enhancing their experience
with the Domino’s brand.

Accurately forecast future business outcomes
Forecasting technology used by Amazon.com | multiple time-series data
forecast scheduling & visualization | supply chain integration
A M A Z O N
F O R E C A S T
NEW

Increase efficiency with automated document analysis
optical character recognition (OCR) | automatic data extraction | natural language processing
intelligent search | text analytics
A M A Z O N
C O M P R E H E N D &
A M A Z O N
C O M P R E H E N D
M E D I C A L
A M A Z O N
T E X T R A C T
NEW

ML was too complicated for everyday developers
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
1
2
3
Train and
tune model
(trial and error)
Set up and
manage
environments
for training
Deploy model
in production
Scale and manage
the production
environment

Custom machine learning for your business
Amazon SageMaker
Fast & accurate
data labeling
Built-in, high
performance
algorithms &
notebooks
BUI L D
1
One-click
training and
tuning
T R AI N
Model
optimization
2
Fully managed
hosting with
auto-scaling and
elastic inference
One-click
deployment
D E P L O Y
3

Custom machine learning for your business
One-click
model training
& deployment
10x
better algorithm
performance
Train once
run anywhere
AMAZON SAGEMAKER
70%
cost reduction for data
labeling using Ground Truth
2x
performance increases from
model optimization with Neo
75%
cost reduction for inference
with Elastic Inference
R E D UCE CO S T S I NCR E ASE P E R FORMANCE E AS E - O F- USE

Advancing pharma research
& discovery
Celgene uses Apache MXNet on Amazon
SageMaker for toxicology prediction to virtually
analyze biological impacts of potential drugs
without putting patients at risk. A model that once
took 2 months to train can now be trained in 4
hours.

Fueling product
innovation
Using Amazon SageMaker, Intuit developed ML
models that can pull a year’s worth of bank
transactions to find deductible business expenses
for customers. Using SageMaker, Intuit reduced
machine learning deployment time by 90%, from 6
months to 1 week.

Driving better
healthcare outcomes
Using Amazon SageMaker, GE Healthcare
developed an ML model that can learn from
thousands of medical scans to detect anomalies
more accurately and efficiently, allowing
radiologists to prioritize patients needing
immediate attention.

Optimizing supply chain
operations
Using Amazon SageMaker, Convoy builds and
trains machine learning models to optimize
driver schedules and to ensure load balancing,
capacity planning, pricing and payments.

Accelerating
financial analysis
Using TensorFlow on Amazon SageMaker,
Siemens Financial Services developed an
NLP model to extract critical information to
accelerate investment due diligence, reducing
time to summarize diligence documents from 12
hours down to 30 seconds.

Highest-performing infrastructure for your business
Fastest and lowest-cost
compute options for ML
workloads
Elastic compute to provision
just-right compute for your ML
workloads
Build custom algorithms using
the ML frameworks
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

The best place to run TensorFlow
Amazon SageMaker is the best
place to run TensorFlow in the
cloud
S T O C K T E N S O R F L O W
A W S - O P T I M I Z E D T E N S O R F L O W
• Fully-managed training and hosting
• Near-linear scaling across 100s of GPU
• 75% lower inference costs with Amazon
Elastic Inference
• 3x faster network throughput with EC2 P3
65%
90%
S c a l i n g e f f i c i e n c y w i t h 2 5 6 G P U s

The Amazon ML stack: Broadest & deepest set of capabilities
A I S E R V I C E S
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
M E D I C A L
L E XR E K O G N I T I O N
V I D E O
Vision Speech Chatbots
A M A Z O N
S A G E M A K E R
B U I L D T R A I N
F O R E C A S TT E X T R A C T P E R S O N A L I Z E
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization (N E O )
One-click deployment & hosting
M L S E R V I C E S
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
Reinforcement learning
Algorithms & models ( A W S M A R K E T P L A C E
F O R M A C H I N E L E A R N I N G )
Language Forecasting Recommendations

Store & retrieve
your data
Comprehensive
analytics
Protect
your data
Delivers
99.999999999%
durability
Up to 400%
faster data
queries
Deepest set of security
&
encryption features
The broadest & deepest capabilities for machine learning

Culture
Setting your
organization up for
success

Assess your structured
and unstructured
data sources
Create
the loop
1.
Connect technology
initiatives with
business outcomes
Advance your
data strategy
Put machine learning
in the hands of
your developers
Organize
for success
2. 3.
?

Evo Car Share
Evo Members 130,000
Evo Trips Per Day 10,000
Evo Car Count 1,500
Years Since launch 4.5

Problem Statement
A % of the fleet sits for hours or days in
undesirable locations, with zero
trips/revenue.
Evo relocates approx. 5% of it’s fleet every
day, moving cars from low demand areas to
high demand areas.
Reactive not proactive.

AWS Solution
Create a prototype that demonstrates a
predictive machine learning model based
on supply, demand, and locality of the Evo
fleet à utilize daily predictions to reduce
percentage of cars within the fleet with 0 or
1 trips per day.
The output should result in a measurable
increase in fleet utilization, drive customer
convenience and increase revenue.

Applying Machine Learning
How can we define a prediction model to identify the vehicles most likely to be
underutilized the next day?
1. Use 5 million recorded trips
2. Define underutilized vehicles (0 or 1 trips the next day)
3. Clean the data and define a trip
4. Both raw and predicted data – daily folders in S3 bucket
5. Data organized geographically into bins that represent 50 m2 areas
6. Multiple supervised learning algorithms (SARIMZ, Polynomial regression, random forest
regression were implemented and tuned to predict under utilization
à Best results: combining the polynomial regression and random forest models

MLSolutionsLab
Brainstorming
Custom modeling
Training
Work side-by-side with Amazon experts
Practical education on ML for new & experienced practitioners
Based on the same material used to train Amazon developers
How we can help…
AWS Machine Learning University

ml.aws

Drive Digital Transformation Using AI

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