Speaker: Barnam Bora, Head of AI/ML, APAC, AWS Customer Speaker: Guangda Li, Co-founder & CTO, ViSenze AWS 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.

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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

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AI is the New Normal

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AI is the New Normal – Why?
2018
WW GDP
$ 80 Tn

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2030
WW GDP
*projected
$ 112 Tn
-Sizing the prize
PwC’s Global Artificial
Intelligence Study:
Exploiting the AI Revolution
27 June 2017
2018
WW GDP
$ 80 Tn
AI is the New Normal – Why?

6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AI is the New Normal – Why?
2030
WW GDP
*projected
$ 112 Tn
$ 15.7 Tn
AI
-Sizing the prize
PwC’s Global Artificial
Intelligence Study:
Exploiting the AI Revolution
27 June 2017
2018
WW GDP
$ 80 Tn
-Sizing the prize
PwC’s Global Artificial
Intelligence Study:
Exploiting the AI Revolution
27 June 2017

7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AI is the New Normal – Why?
2030
WW GDP
*projected
$ 112 Tn
$ 15.7 Tn
AI
The BUILDERS who embrace AI will be the engines of human acceleration.
14% OF
Human
Productivity
in 2030
2018
WW GDP
$ 80 Tn
-Sizing the prize
PwC’s Global Artificial
Intelligence Study:
Exploiting the AI Revolution
27 June 2017

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Defining AI - Popularly
When a machine mimics
"cognitive" functions
that we associate with
other humans,
such as "learning"
and "problem solving".

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Defining AI –
Computer Science
The study of
"intelligent agents":
i.e. any human-made agent that
perceives its environment and takes
actions that maximize its chances of
success at some defined goal.

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Data Science vs AI vs Machine Learning vs Deep Learning
Deep Learning
Machine Learning
Data Science
Artificial Intelligence

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Defining Machine Learning for Business Outcomes
Relevant Data
Domain Expertise
Intelligent Business Systems
& Experiments
Automate - Repeated Decisions
Assist - Human Decisions
Prescribe - Process Improvements
Disrupt - Build Net-New Outcomes
B
U
S
I
N
E
S
S
V
A
L
U
E
Predict - Future Behavior

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Physics
Chemistry
Math
Civil
Electives
Physics
Chemistry
Math
Mechanical Engineer Civil Engineer
Transfer Learning
Mechanical
Electives

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k-means Clustering
k-Nearest Neighbour (k-NN)
PCA
Neural Topic Modelling
Factorisation Machines
Linear Learner – Regression
XGBoost
LDA
Image Classification
Object Detection
Seq2Seq
Linear Learner
Binary Classification
DeepAR Forecasting
ML Prediction now becomes a RESTful API call
Amazon Sagemaker: Built-In Algorithms

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Our Mission in AWS is to put
Machine Learning in the hands
of every Developer

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Few Years Ago

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2 Years Ago

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Every day we apply new AI/ML based
improvements to the Amazon business,
at a global scale through AWS

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Amazon Scout, a Machine Learning powered visual shopping tool for
personalized Recommendations …. www.amazon.com/scout

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Challenges that have so-far prevented organizations from
adopting Machine Learning quickly?
ML
expertise is
rare
Building and
scaling ML
technology is hard
Deploying and
operating models in
production is time-
consuming and
expensive
A lack of cost-
effective,
easy-to-use, and
scalable ML services

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Typical ML Workflow – Customer Pain Points
Data Acquisition & Storage1
Model & Framework selection3
Model Training4
Hyper parameter tuning5
Model testing and simulation6
Model Deployment (inference)7
Data Labelling2
• Iterative and time consuming.
• Image recognition/ object detection
require training with over a million
images with each image containing
over 65,000 pixels.
• Speech recognition and synthesis
require training with 100s of hours of
human speech.
Training
26

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Customers want to Train & Run
models in the cloud at scale
Customers want to Run
models at the edge
AWS IoT Platform
Tesla V100
120 TFLOPS
AWS
Greengrass
Amazon
SageMaker
Amazon
EC2

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We set out to build a
Machine Learning platform that is
accessible to every Developer,
Data Scientist & IT Professional

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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

30. © 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