This document summarizes Amazon Web Services' machine learning services: - It describes Amazon's mission to put machine learning in the hands of every developer and data scientist. - It outlines Amazon's ML stack including API-driven services, the AWS ML platform for deploying models, and frameworks for developing sophisticated models. - It provides examples of Amazon's ML services including Rekognition for computer vision, Polly for text-to-speech, Translate for language translation, Transcribe for speech recognition, Comprehend for natural language processing, Lex for conversational interfaces, and SageMaker for building and deploying machine learning models.

1. Enhancing Your Startup w/
Amazon AI
Mackenzie Kosut, Global Startup Evangelist

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Amazon AI

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ML @ AWS: Our Mission
Put machine learning in the hands of every
developer and data scientist.

8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Application
Services
Platform
Services
Frameworks
&
Infrastructure
API-driven services: Vision & Language Services, Conversational Chatbots
AWS ML Stack
Deploy machine learning models with high-performance machine learning
algorithms, broad framework support, and one-click training, tuning, and
inference.
Develop sophisticated models with any framework, create managed, auto-
scaling clusters of GPUs for large scale training, or run inference on trained
models.

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Customer Running ML on AWS Today

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Application
Services
API-driven services: Vision & Language Services, Conversational Chatbots
AWS ML Stack

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Amazon Rekognition
Deep learning-based visual analysis service

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Amazon
Rekognition
Object and scene detection
Facial analysis
Face comparison
Celebrity recognition
Image moderation
Deep learning-based visual analysis service

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Object & Scene Detection

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Facial Analysis

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Crowd-Mode Face Detection

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Facial Search

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Explicit Nudity
Nudity
Graphic Male Nudity
Graphic Female Nudity
Sexual Activity
Partial Nudity
Suggestive
Female Swimwear or Underwear
Male Swimwear or Underwear
Revealing Clothes
Image Moderation

18. Celebrity Recognition

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Text in Image

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Rekognition API example
aws rekognition detect-labels
–-image '{"S3Object":{"Bucket":"adhorn-reko","Name":"horse.jpg"}}'
{
"Labels": [
{
"Confidence": 99.29136657714844,
"Name": "Human"
},
{
"Confidence": 99.29136657714844,
"Name": "People"
},
{
"Confidence": 99.29136657714844,
"Name": "Person"
},
……

21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Rekognition API example
aws rekognition detect-faces
--image '{"S3Object":{"Bucket":"adhorn-reko","Name":"horse.jpg"}}'
--attributes "ALL”
{
"FaceDetails": [
{
"BoundingBox": {
"Width": 0.05462963134050369,
"Top": 0.2880098819732666,
"Left": 0.4722222089767456,
"Height": 0.07292954623699188
}, "Landmarks": [
{
"Y": 0.31606796383857727,
"X": 0.48852023482322693,
"Type": "eyeLeft"
………

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Amazon Rekognition Video
Deep learning-based visual analysis service
(GA)

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Video in. People, activities, and details out.
Objects, scenes, and activities
Person detection and recognition
Person tracking
Celebrity recognition
Inappropriate content detection
Amazon Rekognition Video

24. Rekognition Video Analysis Service

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Rekognition Video API example
aws rekognition start-label-detection
--video '{"S3Object":{"Bucket":"adhorn-reko","Name":"bourne.mp4"}}’
{
"JobId": "a89eeae89ec38d8579a3a0bfc2bbf522ea5a939cdf751df4b3872d04e8394496”
}
aws rekognition get-label-detection
--jobId "a89eeae89ec38d8579a3a0bfc2bbf522ea5a939cdf751df4b3872d04e8394496”

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Amazon Polly
Deep learning-based Text-to-Speech service

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Amazon Polly
“Hejsan! Jag
heter Astrid och
läser upp det som
skrivs här.”
Amazon Polly: Text In, Life-like Speech Out

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<speak xml:lang="en-US">
The price of this book is <prosody rate="60%">€45</prosody>
</speak>
A Focus On Voice Quality & Pronunciation
Support for Speech Synthesis Markup Language (SSML) Version 1.0
https://www.w3.org/TR/speech-synthesis

31. Polly API example
aws polly synthesize-speech
--text "It was nice to live such a wonderful live show"
--output-format mp3
--voice-id Joanna
--text-type text johanna.mp3
aws polly synthesize-speech
--text-type ssml
--text file://ssml_polly
--output-format mp3
--voice-id Joanna speech.mp3

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Amazon Translate
Neural Machine Translation Service
(Preview Today)

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“Hello, what’s up? Do you
want to go see a movie
tonight?”
Amazon Translate
Natural and fluent language translation
"Bonjour, quoi de neuf ? Tu
veux aller voir un film ce
soir ?"
Amazon
Translate

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Automatically translates text between languages
Real-time translation Powered by deep
learning
12 Language pairs
(more to come)
Language detection

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Translate API example
aws translate translate-text
--endpoint-url https://translate.us-east-1.amazonaws.com
--region us-east-1
--text "hello, what’s up? Do you want to go see a movie tonight?"
--source-language-code "en"
--target-language-code "fr”
{
"TargetLanguageCode": "fr”,
"TranslatedText": "Bonjour, quoi de neuf ? Tu veux aller voir un film ce soir ?”,
"SourceLanguageCode": "en”
}

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Translate API example
aws translate translate-text
--endpoint-url https://translate.us-east-1.amazonaws.com
--region us-east-1
--text "hello, what’s up? Do you want to go see a movie tonight?"
--source-language-code "en"
--target-language-code "fr”
{
"TargetLanguageCode": "fr”,
"TranslatedText": "Bonjour, quoi de neuf ? Tu veux aller voir un film ce soir ?”,
"SourceLanguageCode": "en”
}
Context Awareness

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DEMO – Translation service

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Amazon Transcribe
Automatic speech recognition service
(Preview Today)

39. “Hello, this is Allan
speaking”
Automatic speech recognition service
Amazon
Transcribe

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Available in
preview
today
Support for
telephony audio
Timestamp
generation
Intelligent
punctuation and
formatting
Recognize
multiple
speakers
Custom
vocabulary
Multiple
languages
Automatic speech recognition service

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Amazon Comprehend
Natural Language Processing
(GA)

42. Fully managed natural language processing
Discover valuable insights from text
Entities
Key Phrases
Language
Sentiment
Amazon
Comprehend

43. Support for large data sets and topic modeling
STORM
WORLD SERIES
STOCK MARKET
WASHINGTON
LIBRARY OF
NEWS ARTICLES *
Amazon
Comprehend
* Integrated with Amazon S3 and AWS Glue

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Comprehend API example
aws comprehend detect-sentiment
--text "I love you"
--language-code "en”
{
"SentimentScore":
{
"Mixed": 0.005664939060807228,
"Positive": 0.9262985587120056,
"Neutral": 0.06511948257684708,
"Negative": 0.0029170133639127016
},
"Sentiment": "POSITIVE”
}

47. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon Lex
Conversational Interfaces

48. Intents
A particular goal that the
user wants to achieve
Utterances
Spoken or typed phrases
that invoke your intent
Slots
Data the user must provide to fulfill the
intent
Prompts
Questions that ask the user to input
data
Fulfillment
The business logic required to fulfill the
user’s intent
BookHotel

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Lex Bots
Salesforce
Microsoft Dynamics
Marketo
Zendesk
Web
Devices
Apps
Facebook Messenger,
Slack,
Amazon
Connect
Mobile
Mobile Hub
integration
Quickbooks
Amazon Lex: Conversational Chatbots

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Platform
Services
AWS ML Stack
Deploy machine learning models with high-performance machine learning
algorithms, broad framework support, and one-click training, tuning, and
inference.

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Amazon EMR
Easily Run and Scale Apache Hadoop, Spark,
HBase, Presto, Hive, and other
Big Data Frameworks

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ML Applications on Amazon EMR
Amazon EMR
(Elastic MapReduce)

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Amazon SageMaker
A fully managed service to quickly and easily
build machine-learning based models
(GA)

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End-to-End
Machine Learning
Platform
Zero setup Flexible Model
Training
Pay by the second
$
Amazon SageMaker
Build, train, and deploy machine learning models at scale

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Highly-optimized
machine learning
algorithms
BuildPre-built
notebook
instances
Amazon SageMaker

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Highly-optimized
machine learning
algorithms
One-click training
for ML, DL, and
custom algorithms
BuildPre-built
notebook
instances
Easier training with
hyperparameter
optimization
Train
Amazon SageMaker

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One-click training
for ML, DL, and
custom algorithms
Easier training with
hyperparameter
optimization
Highly-optimized
machine learning
algorithms
Deployment
without
engineering effort
Fully-managed
hosting at scale
BuildPre-built
notebook
instances
Deploy
Train
Amazon SageMaker

58. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Frameworks
&
Infrastructure
AWS ML Stack
Develop sophisticated models with any framework, create managed, auto-
scaling clusters of GPUs for large scale training, or run inference on trained
models.

59. AWS Deep Learning AMI
• Easy-to-launch tutorials
• Hassle-free setup and configuration
• Pay only for what you use
• Accelerate your model training and deployment
• Support for popular deep learning frameworks

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Amazon ML Lab
Provides the missing ML expertise

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Amazon ML Lab
Lots of companies
doing Machine
Learning
Unable to unlock
business potential
Brainstorming Modeling Teaching
Lack ML
expertise
Leverage Amazon experts with decades of ML
experience with technologies like Amazon Echo,
Amazon Alexa, Prime Air and Amazon Go
Amazon ML Lab
provides the missing
ML expertise

62. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Summary

63. FRAMEWORKS AND INTERFACES
PLATFORM SERVICES
APPLICATION SERVICES
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
Democratization of AI
Amazon
Rekognition
Video
Amazon
Transcribe
Amazon
Comprehend
Amazon
SageMaker
AWS DeepLens Amazon EMR
Deep Learning
AMI
Amazon
Translate

64. G O B U I L D