Machine Learning Analytics for the rest of us

P U B L I C S E C T O R
S U M M I T
Canberra, ACT

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R
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Machine Learning Analytics for the
Rest of Us
Jenny Davies
Solutions Architect
AWS

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

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Some of our machine learning customers…

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Extracting data from images or scanned archives

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Identify objects, people, text, scenes, and activity, as well
as any inappropriate content in images and video

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Scenario
Determine the license plate of
the most prominent car in
frame

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Step 1: Find all cars
Use Amazon Rekognition API to
find cars in image
% pseudo code %
response = rekognition.detect_labels()
cars = response['Labels']) where
response['Labels']['Name'] = 'Car'

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Step 2: Determine
largest bounding box
% pseudo code %
for car in cars[‘Instances’]:
width = car['BoundingBox']['Width’]
height = car['BoundingBox']['Height’]
area = height * width

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Step 3: Read text from
most prominent car
Read text and use logic to
determine license plate
% pseudo code %
response = rekognition.detect_text()
Returns detected text, inferred
text type, and bounding box

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

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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

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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 …

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Amazon Textract table detectionAmazon Textract
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%

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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

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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 forms

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Comprehension, forecasts, and predictions from your
data

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

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No master algorithm for personalization and
recommendation
Music Film Products Content

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Amazon Personalize: How it works
Amazon Personalize

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Amazon Personalize
Improve customer experiences with personalization and recommendations
K E Y F E A T U R E S
Context-aware
recommendations
Automated
machine learning
Bring existing algorithms
from Amazon SageMaker
Continuous learning
to improve
performance

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

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Sales
Challenges in forecasting

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Challenges in forecasting
Sales

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Amazon Forecast: How it works
Amazon Forecast

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

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Forecasting, anomaly detection, and auto-narratives

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Amazon Quicksight ML
Three
powerful new
features
ML Anomaly Detection
Uncover hidden insights by
continuously analysing across
billions of data points

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Three
powerful new
features
ML Forecasting
Predict key business metrics
with point and click simplicity

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Three
powerful new
features
ML Forecasting
Predict key business metrics
with point and click simplicity
Auto-narratives
Tell the story of your data in
plain language narratives

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Date Product Category Geo Revenue
17/2/18 Electronics Australia $ 56.75
17/2/18 Outdoors Australia $ 8,876.88
17/2/18 Business Australia $ 391.34
17/2/18 Crafts Australia $ 0.66
17/2/18 Office Supplies Argentina $ 23,049.62
17/2/18 Collectibles UK $ 115.38
17/2/18 Baby Products UK $ 1,842.82
17/2/18 Software UK $ 75.64
17/2/18 Baby Products UK $ 41,424.23
17/2/18 Financial Services UK $ 3,946.44

Machine Learning Analytics for the rest of us

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