Amazon brings computer vision, natural language processing (NLP), speech recognition, text to speech, and machine translation within the reach of every developer. API-driven application services enable developers to easily plug in pre-built artificial intelligence (AI) functionality into their applications, and to automate manual workflows. In this session, we will share how to build the next generation of intelligent apps that can see, hear, speak, understand, and interact with the world around us.Automating the provisioning, configuration and deployment of complex applications requires some design choices on top of AWS services. This presentation discusses how to implement modularity, reliability and security into continuous delivery pipelines ("DevSecOps"). Learn how to automate application delivery using AWS CloudFormation and other tools from Amazon Web Services.

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STEVEN BRYEN | AWS TECHNICAL & DEVELOPER EVANGELISM | @steven_bryen
sbryen@amazon.com
LONDON – MARCH 2019
AML1
Build Intelligent Apps using AI Services

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

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Our Approach for Machine Learning
Customer-focused
90%+ of our ML roadmap is
defined by customers
Multi-framework
Support for the most
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

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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
The Amazon ML Stack: Broadest & Deepest Set of Capabilities
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 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 TT E X T R A C T P E R S O N A L I Z E
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization (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
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 learningAlgorithms & 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 )
Language Forecasting Recommendations

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AI Services
Pre-trained AI services that require
no ML skills or training
Easily add intelligence to your
existing apps and workflows
Quality and accuracy from
continuously-learning APIs
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 Chatbots
F O R E C A S TT E X T R A C T P E R S O N A L I Z E
Language Forecasting Recommendations

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Audio Input
Example: End-to-end audio analysis
store
Amazon S3
trigger
AWS
Lambda
call
Amazon
Transcribe
in cascade
Amazon
Comprehend
aggregate
Amazon
Athena
analyze
Amazon
QuickSight

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Example: Media analysis solution
S3:
Web UI
aws.amazon.com/answers/media-entertainment/media-analysis-solution/
S3:
Media storage
Step Functions:
Orchestrate
analysis
Elasticsearch:
Search index
Amazon Rekognition Video:
Detect objects, scenes,
faces, & celebrities
AWS Elemental MediaConvert:
Transcode videos
Transcribe Comprehend
Lambda
API Gateway:
REST API
Lambda

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Amazon Rekognition – Image and Video Analysis

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Optical Character Recognition (OCR)

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Amazon Textract – OCR++

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Amazon Textract – How it works

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Amazon Lex
A service for building conversational interfaces into your
applications using voice and text

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Amazon Lex – Features
Text and speech language understanding: powered by
the same technology as Amazon Alexa
Deployment to chat services
(Web/Mobile Apps, Facebook, Kik, Slack, Twilio SMS)
Designed for builders: efficient and intuitive tools to
build conversations; scales automatically
Versioning and alias support@

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Amazon Lex Bots – key concepts
Utterances
Spoken or typed phrases that invoke
your intent
BookHotel
Intents
An intent performs an action in response
to natural language user input
Slots
Slots are input data required to fulfill
the intent
Fulfillment
Fulfillment mechanism for your intent

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“Book a hotel”
Book hotel
NYC
“Book a hotel in
NYC”
Automatic speech recognition
Hotel booking
New York City
Natural language
understanding
Intent/slot
Model
UtterancesHotel Booking
City New York City
Check in Nov 30th
Check out Dec 2nd
“Your hotel is booked for Nov
30th”
Amazon Polly
Confirmation: “Your hotel is
booked for Nov 30th”
“Can I go ahead
with the booking?
a
in

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Utterances
I’d like to book a hotel
I want to make my hotel
reservations
I want to book a hotel in New
York City
Can you help me book my hotel?

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Slots
Destination City New York City, Seattle, London …
Slot Type Values
Check in Date Valid dates
Check out Date Valid dates

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Slot elicitation
I’d like to book a hotel
What date do you check in?
New York City
Sure, what city do you want to book?
Nov 30th Check in
11/30/2017
City
New York City

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Amazon Connect
Self-service, cloud-based contact center service
Real time and
historical analytics
High-quality
voice capability
Call
recording
Skills-based routing
[Automatic Call Distribution (ACD)]

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Intelligent call center chatbot
Amazon
Connect
Customer
Amazon Lex Lambda:
Fulfillment
DynamoDB:
Customer Data
SNS:
SMS Messaging
Customer calls
Connect to
reschedule an
appointment
Connect calls
Lex chatbot
Lex chatbot calls
Lambda function
to get customer
preferences and
fulfil Intents
Lambda function
sends text message
confirmation via SNS
Customer receives
appointment
confirmation text
message
Lambda
function
writes updates
to DynamoDB

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Turn text into lifelike speech using deep learning

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Amazon Polly
• Content creation
• Mobile & desktop applications
• Internet of Things (IoT)
• Education & e-learning
• Telephony
• Game development
Use cases
• 58 voices across 28 languages
• Lip-syncing & text highlighting
• Fine-grained voice control
• Custom vocabularies
• Available in 18 AWS Regions
Key features

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Amazon Polly
“Hi, my name is
Steve…”
Text-to-speech (TTS)

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Synthesize Speech API
$ aws polly synthesize-speech
--text "hello"
--voice-id Matthew
--output-format mp3
[--lexicon-names mylex1 mylex2]
output.mp3
{
"ContentType": "audio/mpeg",
"RequestCharacters": "11"
}

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“With Amazon Polly our users benefit from
the most lifelike Text-to-Speech voices
available on the market.”
Severin Hacker
CTO, Duolingo

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Turn speech into text

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“Hello, this is Allan
speaking”
Amazon
Transcribe
Speech-to-text (STT)

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English
Italian
French
Spanish
Portuguese
Supported languages *
* more languages coming soon!

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ringDNA
End-to-end communications platform for
sales teams
Enterprise organizations use RingDNA to
dramatically increase productivity, engage
in smarter sales conversations, gain
predictive sales insights and improve their
win rate
Speech to Text
"A critical component of RingDNA’s Conversation AI
requires best of breed speech-to-text to deliver
transcriptions of every phone call. RingDNA is
excited about Amazon Transcribe since it
provides high-quality speech recognition at scale,
helping us to better transcribe every call to text "
Howard Brown, CEO & Founder, RingDNA
https://www.youtube.com/watch?v=1ZJ_f1bDdog

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How do you extract insights from
unstructured text?

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Amazon Comprehend
A fully managed and continuously trained service that helps you
extract insights from unstructured text

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Amazon Comprehend
Sentiment Entities LanguagesKeyphrases
Topic
modeling
Syntax

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Amazon Comprehend – Natural Language Processing
Amazon.com, Inc. is located in Seattle, WA
and was founded July 5, 1994 by Jeff
Bezos. Our customers love buying
everything from books to blenders at
great prices
Named Entities
Amazon.com: Organization
Seattle, WA : Location
July 5th,1994: Date
Jeff Bezos : Person
Keyphrases
Our customers
books
blenders
great prices
Sentiment
Positive
Language
English

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Amazon Comprehend – Syntax API
Our customers love buying everything
from books to blenders at great prices
Token
(word)
Part of
Speech
customers Noun
love Verb
books Noun
great Adjective
prices Noun

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Supported parts of speech
ADJ – Adjective
ADP – Adposition
ADV – Adverb
AUX – Auxiliary
CCONJ – Coordinating Conjunction
DET – Determiner
INTJ - Interjection
NOUN - Noun
NUM – Numeral
O – Other
PART – Particle
PRON – Pronoun
PROPN – Proper Noun
PUNCT – Punctuation
SCONJ – Subordinating
Conjunction
SYM – Symbol
VERB – Verb

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Syntax detection
$ aws comprehend detect-syntax
--language-code 'en' --text 'I love cloud!’
{
"SyntaxTokens": [
{
"TokenId": 1,
"Text": "I",
"BeginOffset": 0,
"EndOffset": 1,
"PartOfSpeech": {
"Tag": "PRON",
"Score": 0.9999802112579346
}
},
...

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Sentiment Analysis
$ aws comprehend detect-sentiment
--language-code 'en' --text 'I love cloud!’
{
"Sentiment": "POSITIVE”,
"SentimentScore": {
"Mixed": 0.012617903761565685,
"Positive": 0.9599817991256714,
"Neutral": 0.021758323535323143,
"Negative": 0.005641999188810587
}
}

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Popular text analytics use cases
Content Personalization
• Understand related documents based on entities, phrases or even topic similarities for trends
analysis, to drive content personalization and recommendations
Semantic Search
• Index entities for boosting and ranking search results
Intelligent data warehouse
• Query unstructured data in relational databases, processing data within the data lake (Amazon S3)
and then inserting it back into the data warehouse
Social Analytics
• Ingest, process and analyze trends from entities and sentiment from social media posts across
Twitter and Facebook

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Support for large data sets and topic modeling
STORM
WORLD SERIES
STOCK MARKET
WASHINGTON
LIBRARY OF
NEW S AR TICLES *
Amazon
Comprehend

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Extract information from unstructured medical text accurately and quickly
No machine learning experience required
Amazon Comprehend
Medical

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Yes, natural language translation J

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Supported languages *
Arabic
Simplified Chinese
French
German
Spanish
Portuguese
Japanese
Traditional Chinese
Italian
Russian
Turkish
Czech
* 417 translation combinations
Danish
Dutch
Finnish
Swedish
Polish
Hebrew

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«We operate 90 localized websites in 41
languages. (…)
Having evaluated Amazon Translate
and several other solutions, we believe
that Amazon Translate presents a quick,
efficient and most importantly, accurate
solution. »
Matt Fryer, VP and Chief Data Science Officer, Hotels.com

51. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

52. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
STEVEN BRYEN | AWS TECHNICAL & DEVELOPER EVANGELISM | @steven_bryen
sbryen@amazon.com
LONDON – MARCH 2019
Thank You!