Machine Learning at Amazon

July 10-11, 2019
The Conference Center,

Machine Learning at Amazon
Transforming Education
Balaji Iyer
Business Development Manager, ML
Worldwide Public Sector, AWS

3© 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Why is Machine Learning important?

Some numbers
• 90% of all data today was created in the last two years.
• 1000% growth in data in the past two years.
• ~1.5% growth in US population in the past two years.
Most of the data is untapped… and we can’t grow our workforce fast
enough to keep up with data growth.
Machine learning is the technology that enables human expertise to be
learned and applied in a scalable way.

40% of digital transformation initiatives
supported by ML in 2019
—IDC 2018
InnovationDecision
making
Customer
experience
C E N T E R P I E C E F O R E D U C A T I O N T R A N S F O R M A T I O N
Business
operations
Competitive
advantage

How is ML transforming education?

I N C R E A S E T H E R E A C H A N D I M P A C T O F L E A R N I N G C O N T E N T
2 Million students globally
engage with course video
Students focus on class time rather than
notetaking using Amazon Transcribe making
learning assets more valuable and accessible to
students.

I M P R O V E A C C E S S I B I L I T Y F O R S T U D E N T S O F A L L A B I L I T I E S
60-year old assessment
organization in the US
Blind candidates get “auditory” signal of timing
followed by messages letting you know how
many minutes you have left in a section of the
test section using Amazon Polly.

Experiment with multiple algorithms using Amazon
SageMaker, tuned the models using hyperparameter
optimization, and automated the deployment of
machine learning (ML) models.
Curate educational content
to help libraries scale
P E R S O N A L I Z E D E X P E R I E N C E

Language-learning platform for
170M users
Provide text-to-speech functionality and high-quality
language education for reliably serving audio to millions of
language learners every day.
R E A C H A G L O B A L A U D I E N C E W I T H E A S E

Recommend new test
date for the SAT
Generate and analyze many millions of calendars
using Amazon SageMaker and created an August
date for SAT. It proved so popular that there
weren't enough seats the first year.
“In our nearly 100 years of doing the SAT, we had
never considered August.”
A D A P T T O C H A N G I N G S T U D E N T N E E D S
No PII was used in this analysis.

H E L P C U S T O M E R S C O M M U N I C A T E M O R E E F F E C T I V E L Y
Natural Language Processing helps
millions with writing assistance
Amazon SageMaker makes it possible to develop TensorFlow
models in a distributed training environment. Run inferences
of our mobile device implementations for iOS and Android
customers.

Scaling real-time translation for
thousands of customers
Lionbridge uses Amazon Translate to scale its
translation and localization services, achieving
cost efficiencies of up to 20%.
R E A L - T I M E T R A N S L A T I O N

Complete genomics
research in hours
SF State uses machine learning to predict functional sites in
proteins and other three-dimensional (3D) molecular
structures.
“Amazon helps us lift the ceiling on the size and scope of our
machine learning experiments.”
A C C E L E R A T E R E S E A R C H

Discover underlying themes in a
document collection
Researchers at Clemson conducted nearly half a million topic
modeling experiments to study how human language is
processed by computers. The 1.1 Million vCPU count usage is
comparable to the core count on the largest supercomputers
in the world.
A C C E L E R A T E R E S E A R C H

Cutting the time for translating
medical terminologies by 80%
Historically, automated translation never handled medically
terminology well, but early use of Amazon Translate shows
to handle complex terms across six languages naturally.
T R A N S L A T E M E D I C A L T E R M I N O L O G I E S

How can Amazon help me succeed?

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

FRAMEWORKS INTERFACES INFRASTRUCTURE
ML Services
Broadest and deepest set of capabilities
T H E A W S M L S T A C K
VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS
ML Frameworks + Infrastructure
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
A M A Z O N
L E X
F O R E C A S T
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
R E K O G N I T I O N
V I D E O
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
Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment HostingAmazon SageMaker
F P G A SA M A Z O N E C 2 P 3
& P 3 D N
A M A Z O N
E C 2 G 4
A M A Z O N
E C 2 C 5
I N F E R E N T I AA W S I o T
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

10,000+ customers | 2x the customer references | 85% of TensorFlow projects in the cloud is on AWS

What does an ideal ML workflow look like?

Bringing machine learning to all developers
A M A Z O N S A G E M A K E R
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

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
Pre-built
notebooks for
common problems

Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
• K-Means clustering
• Principal component analysis
• Neural topic modelling
• Factorization machines
• Linear learner (regression)
• BlazingText
• Reinforcement learning
• XGBoost
• Topic modeling (LDA)
• Image classification
• Seq2Seq
• Linear learner (classification)
• DeepAR forecasting

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
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
training

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
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
training Optimization

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
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
One-click
deployment

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
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
One-click
deployment
Fully managed with
auto-scaling, health
checks, automatic handling
of node failures,
and security checks

One-click
model training
and deployment
Train once
run anywhere
10x
better algorithm
performance
2x
performance increases from
model optimization with Neo
70%
cost reduction for data
labeling using Ground Truth
75%
cost reduction for inference
with Elastic Inference
R E D U C E C O ST S IN C R E A SE P E R F O R M A N C E E A SE -O F -U SE
C U S T O M M A C H I N E L E A R N I N G F O R Y O U R B U S I N E S S
AMAZON SAGEMAKER

What do I need to succeed with ML?

It starts with the right question...
Use Case Data Model
Identify the problem
you’re trying to solve
Choose a small, well
understood dataset
Lots of iteration…

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

• Build machine learning models
in Amazon SageMaker
• Train, test, and iterate on the track using
the AWS DeepRacer 3D racing simulator
• Compete in the world’s first global autonomous
racing league to race for prizes and a chance to
advance to win the coveted AWS DeepRacer Cup
A fully autonomous 1/18th-scale race car designed to help you
learn about reinforcement learning through autonomous driving
A W S D E E P R A C E R

The world’s first deep learning-
enabled video camera for developers
A W S D E E P L E N S
• Purpose-built for ML skills development
• Fully programmable and customizable
• Build custom Amazon SageMaker models
• Ten minutes to your first deep learning project

Amazon Machine Learning University
Uses the same
materials used to train
Amazon developers
Foundational
knowledge with
real-world application
Structured
courses and
specialist certification
https://aws.training/machinelearning

Amazon Machine Learning University - Free Digital Training

H O W W E C A N H E L P
• Brainstorming
• Custom modeling
• Training
• Work side-by-side with Amazon experts
ML Solutions Lab
• Practical education on ML for new
and experienced practitioners
• Based on the same material used to
train Amazon developers
Machine Learning
Training and Certification

AI.aws
Thank you!
Balaji Iyer
balaiyer@amazon.com

Machine Learning at Amazon

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