Speaker: Ben Snively, Principal Solutions Architect - Data & Analytics, AWS Note: This is Part 1 of the deck. Adding to the existing AI services, AWS continues to bridge the gap for developers to build ML solutions without the hurdle of having data science expertise. In this session learn about the new services announced at re: Invent (Forecast, Textract and Personalize) and get a preview of what to expect when building time series models, OCR and recommendation engines with little to no data science experience.

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Accelerating ML adoption with our
new AI services
Ben Snively
Specialist Solutions Architect
AWS

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Other Machine Learning Breakouts
1400-1440: Build, train and deploy Deep Learning models on
Amazon SageMaker
Pedro Paez
1510-1550: Building Business outcome with
Machine Learning on AWS
Barnam Bora
1620-1700: Internet of Things - beyond the Hype
Timothee Cruse

3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Amazon AI Services
easily integrate with your applications to address
common use cases such as time series forecasting,
document analysis, personalized recommendations and
others. Because we use the same deep learning
technology that powers Amazon.com and our ML
Services, you get quality and accuracy from continuously-
learning APIs with little to no data science experience

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2019
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
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 &
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 Language 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 T
Forecasting
T E X T R A C T
Recommendations
D E P L O Y
Pre-built algorithms
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization (N E O )
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
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 )
I N F E R E N T I A
Notebook Hosting
One-click deployment & hosting
Auto-scaling
Virtual Private Cloud
Private Link
Elastic Inference integration
Hyper Parameter Optimization
P E R S O N A L I Z E

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Amazon Textract
Automatically extract text and data from scanned
documents and go beyond simple optical character
recognition (OCR) to also identify the contents of
fields in forms and information stored in tables.

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Documents and Forms can be complicated…
…not a single corresponding pixel value in common

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Text extraction Table extraction Form extraction
Amazon Textract

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Blocks: PAGE, PARAGRAPH, LINE, WORD
is washed by waves, and cooled
Text extraction Table extraction Form extraction
Amazon
Textract

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Name Description
Blocks List of blocks identified
from the document
ID Unique ID of the unit
Relationships CHILD
Block type PAGE, PARAGRAPH, LINE, WORD
Pages Contains number of
pages in the document
Name Description
Document Blob or Amazon S3
object
Text extraction Table extraction Form extraction
Amazon
Textract

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Blocks: PAGE, TABLE, CELL
For each ’block’ you get:
• Text
• Confidence score
• Block relationships (e.g. cells within a table)
Text extraction Table extraction Form extraction
Amazon
Textract

11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Name Description
Document Blob or Amazon S3
object
FeatureTypes TABLES
Name Description
Blocks List of blocks identified
from the document
ID Unique ID of the unit
Relationships CHILD
Block type PAGE, TABLE, CELL
Pages Contains number of
pages in the document
Text extraction Table extraction Form extraction
Amazon
Textract

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Blocks: PAGE, KEY_VALUE_SET
For each ’block’ of your document:
• Form field name (key) and field value (value) association
• Confidence score
• Page number
• Block relationships
Text extraction Table extraction Form extraction
Amazon
Textract

13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Name Description
Document Blob or Amazon S3
object
FeatureTypes FORMS
Name Description
Blocks List of blocks identified
from the document
ID Unique ID of the unit
Relationships KEY, VALUE, CHILD
Block type PAGE, KEY_VALUE_SET
Pages Contains number of
pages in the document
Text extraction Table extraction Form extraction
Amazon
Textract

14. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Input
Uploaded document
images of notes,
explanation of
benefits, and forms
Amazon S3
Uploaded documents
are stored in S3
NLP
Use natural language
processing to extract
insights
from documents
Amazon
Elasticsearch
Service
Easily search through
extracted data and
text insights
Output
Discover insights to
improve services
Amazon Textract
Automatically extract
words and lines of
text, and tables

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Amazon Personalize
Batch or Real-time personalization and
recommendation service. No ML experience required.