Supercharge Your Organisation With Machine Learning on AWS - AWS Summit Sydney

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Supercharge your organisation
with machine learning on AWS
Ben Thurgood
Senior Manager Solutions Architecture
Amazon Web Services

Customer
experience
Business
operations
Decision
making
Innovation Competitive
advantage
of digital transformation initiatives
supported by AI in 201940% - IDC 2018
Centerpiece for digital transformation

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Put machine learning in the
hands of every developer
Our mission at AWS

Our approach to machine learning
Customer-focused
90%+ of our ML roadmap is
defined by customers
Multi-framework
Support for all
popular frameworks
Pace of innovation
200+ new ML launches and major
feature updates in the
last year
Breadth and depth
A wide range of AI and ML services in-
production
Security and analytics
Deep set of security and
encryption features, with robust
analytics capabilities
Embedded R&D
Customer-centric approach to
advancing the state of the art

customers have used
machine learning on AWS
10,000+
AWS holds the top spots on Stanford’s
deep learning benchmark, DAWN, for
fastest training time, lowest cost, 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

Some of our machine learning customers…

Three ML challenges customers face today
1 2 3

Amazon EC2 P3dn instance
The largest P3 instance, optimised for distributed training
K E Y F E A T U R E S
100Gbps of networking
bandwidth
(4x more than P3)
8 NVIDIA Tesla
V100 GPUs
32GB of
memory per GPU
(2x more P3)
96 Intel
Skylake vCPUs
(50% more than P3)
with AVX-512

The best place to run TensorFlow
Fastest time
for TensorFlow
65% 90%
30m 14m
Available with
Amazon SageMaker
and the AWS Deep
Learning AMIs

1 2
Inference
(Prediction)
90%
Training
10%
Predictions drive
complexity and
cost in production

Amazon Elastic Inference
Reduce deep learning inference costs up to 75%
Integrated with
Amazon EC2 and
Amazon SageMaker
Support for TensorFlow,
Apache MXNet -
PyTorch coming soon
Single and
mixed-precision
operations

AWS Inferentia
High performance machine learning chip, designed by AWS
Support for TensorFlow,
MXNet, and PyTorch
Use with Amazon
EC2, Amazon
SageMaker, and
Elastic Inference
Optimised for
multiple data
types
Combine chips to scale
to thousands of TOPS

In 2017, we launched Amazon SageMaker…
1
2
3

Amazon SageMaker: build, train, and deploy ML
1
2
3
1
2
3
Amazon EC2 P3dn
Instances
Amazon Elastic
Inference

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.

Successful models require high-quality data

Build highly accurate training datasets and reduce data
labeling costs by up to 70% using machine learning

How it works: Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth:
Creating training datasets

Amazon SageMaker Ground Truth
Label machine learning training data easily and accurately
Automatic labeling via
machine learning
Ready-made and
custom workflows
Label
management
Private and public
human workforce

Over 150 algorithms and models that
can be deployed directly to Amazon SageMaker

AWS Marketplace for Machine Learning
ML algorithms and models available instantly
Automatic labeling via machine learning
IP protection
Automated billing and metering
S E L L E R S
Broad selection of paid, free, and
open-source algorithms and models
Data protection
Discoverable on your AWS bill
B U Y E R S

Over 230 algorithms and models
Natural language
processing
Grammar and parsing Text OCR Computer vision
Named entity
recognition
Video classification
Speech recognition Text-to-speech Speaker identification Text classification 3D images Anomaly detection
Text generation Object detection Regression Text clustering
Handwriting
recognition
Ranking
A V A I L A B L E A L G O R I T H M & M O D E L S
S E L E C T E D V E N D O R S

Amazon SageMaker Neo
Train once, run anywhere with 2x the performance
Open-source device runtime and compiler,
1/10th the size of original frameworks

Amazon SageMaker Neo: Train once, run anywhere

Recap: updates to Amazon SageMaker
Collect and
prepare training
data
Train and
tune model
Set up and
manage
environments
for training
Deploy model
in production
Scale and manage
the production
environment
Amazon EC2 P3dn
Instances
Amazon SageMaker
Ground Truth
Amazon Elastic
Inference
AWS Marketplace
for
Machine Learning
Amazon SageMaker
Neo
Amazon SageMaker
Workflows

Customers often ask
“How can we tap into Amazon’s
experience in machine learning?”

Sales
Challenges in forecasting

Challenges in forecasting
Sales

Accurate time-series forecasting service, based
on the same technology used at Amazon.com.
No ML experience required.

Amazon Forecast: How it works
Amazon Forecast

Amazon Forecast
Improve forecasting accuracy by up to 50% at 1/10th the cost
Consider multiple
time-series
at once
Automatic
machine
learning
Visualise forecasts
& import results
into business apps
Evaluate model
accuracy
Schedule
forecasts and
model retraining
Bring existing
algorithms from
Amazon
SageMaker
Privacy &
encryption

No master algorithm for personalisation and
recommendation
Music Film Products Content

Real-time personalisation and recommendation service,
based on the same technology used at Amazon.com.
No ML experience required.

Amazon Personalize: How it works
Amazon Personalize

Amazon Personalize
Improve customer experiences with personalisation and recommendations
Context-aware
Recommendations
Automated
machine learning
Bring existing algorithms
from Amazon SageMaker
Continuous learning
to improve
performance

OCR++ service to easily extract text and data from virtually
any document. No ML experience required.

Traditional OCR Systems
MethOd Num' C'USte'S Rand mdex ated using two
types of measures. The first is the average
TM~score 8 89.7% silhouette width itself, which is a
measure of the clus-
ppm 9 39,396 ter compactness and separation. In
general, clustering is
305C 9 895% based on the assumption that the
underlying data form
compact clusters of similar characteristics. Larger aver-
R50 7 92.096
age Silhouette Width means that the result of a
clustering

Amazon Textract
TEXT method. Then, the proteins were clustered
using the k- medoids method with the
optimal number of clusters.
The performance of the various clusterings
was evalu- ated using two types of measures.
The first is the average silhouette width itself,
which is a measure of the clus- ter
compactness and separation. In general,
clustering is based on the assumption that
the underlying data form compact clusters of
similar characteristics. Larger average
silhouette width means that the result of a
clustering algorithm consists of compact
clusters which are well sep- arated from each
other, i.e. probably close to the actual data
distribution. A small average silhouette width
means e.g. that one of the clusters …

Amazon Textract table detection
TABLE
DATA
Method Num. clusters Rand index
TM-score 8 89.7%
FPFH 9 89.3%
3DSC 9 89.5%
RSD 7 92.0%
VFH 8 85.3%
Combined
silhouette weights
7 92.2%
Combined equal
weights
7 90.2%

Graceland, Memphis
Presley, Elvis Aaron
TCB Limited
12-12-1234
TN
01 08 1935 X
901 987-6543
3765 Elvis Presley Blvd.
38116
X RCA Records
Rock n Roll Health
X
Presley, Elvis Aaron
Government forms (e.g. FDA new drug
application, financial disclosure form,
incident reporting)
Tax forms (US – e.g. W2, 1099-MISC, 990,
1040; UK – e.g. P45; Canada – e.g. T4, T5)
Amazon Textract forms

Amazon Textract forms
Presley, Elvis AaronN A M E
Graceland, Memphis, TNA D D R E S S
12-12-1234I D
TCB LimitedC O M P A N Y
Graceland, Memphis
Presley
TCB Limited
12-12-1234
TN
901 987-6543
3765 Elvis Presley Blvd.
38116
Elvis
Elvis.Presley@yahoo.com

Amazon Textract
Extract text and data from virtually any document
Optical Character
Recognition (OCR)
Key-value pair
detection
Adjustable confidence
thresholds
Table
detection
Bounding box
coordinates
No ML experience
required

In 2017, we launched Amazon Comprehend…
Entities
Key phrases
Language
Sentiment
Amazon
Comprehend
Custom entities
Custom classification

• Lipitor: Brand Name
• 20 mg: Dosage
• Once Daily: Frequency
Amazon Comprehend Medical
Mr. Smith is a 63-year-old gentleman with
coronary artery disease and hypertension.
CURRENT MEDICATIONS: taking a dose of
LIPITOR 20 mg once daily
• Mr. Smith: Name
• 63 : Age
• Coronary artery : System Organ Site
• Coronary artery disease: Diagnosis Name
• Hypertension: Diagnosis Name
Relationship
extraction

Amazon Comprehend Medical
Extract text and data from virtually any document
Medical
conditions
Anatomy
entities
PHI
identification
Medication and
dosage extraction
No ML experience
required

New solutions to three ML challenges customers face today
Cost Data Ease of use
1 2 3

Introducing Reinforcement Learning
Supervised
Learning
(ASR, computer vision)
Unsupervised
Learning
(Anomaly detection,
identifying text topics)

Supervised
Learning
(ASR, computer vision)
Unsupervised
Learning
(Anomaly detection,
identifying text topics)
Introducing Reinforcement Learning
Reinforcement
Learning (RL)

Reinforcement learning
Achieve outcomes, not decisions
Robotics Industrial controls Natural language
systems
Games

How does RL work?

How does RL work?
Extremely complex Expensive
Effectively out of reach

Amazon SageMaker RL
Reinforcement learning for every developer and data scientist
2D & 3D physics
environments and
OpenGym support
Support Amazon Sumerian, AWS
RoboMaker and the open source
Robotics Operating System
(ROS) project
Example notebooks
and tutorials

Introducing AWS DeepRacer

AWS DeepRacer:
How does it work?
3D simulator
with virtual
car and track

AWS DeepRacer League
Competitive racing league for AWS DeepRacer

Amazon ML Solutions Lab
Helping customers accelerate their use of machine learning in products and processes
• Knowledge transfer from
AWS subject matter experts
• Ideation
• Custom modeling
• Operationalising ML

AWS Machine Learning Training and Certification

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
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
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
A M A Z O N
R 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
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 (A M A Z O N G R O U N D T R U T H )
One-click model training & tuning
Optimization ( A M A Z O N S A G E M A K E R 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 C 2 P 3
& P 3 d n
A M A Z O N
E C 2 C 5
F P G A s A W S
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
Models without training data (REINFORCEMENT LEARNING)Algorithms & models ( A W S M A R K E T P L A C E )
Language Forecasting Recommendations
NEW NEWNEW
NEW
NEW
NEWNEWNEW
NEW
RL Coach
The Amazon ML stack

Thank you!
Ben Thurgood
btgood@amazon.com

Supercharge Your Organisation With Machine Learning on AWS - AWS Summit Sydney

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