Introduction to AI on AWS - AI/ML Hebrew Webinar

© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Introduction to
AI on AWS
Boaz Ziniman – Technical Evangelist, AWS
@ziniman
boaz.ziniman.aws
Ready, Set, Go! And beyond
AI/ML webinar series in Hebrew

Agenda
• “Ready” - Introduction to AI/Machine Learning – April 29
Boaz Ziniman
• “Set” - What is ML for developers? – April 30
Guy Ernest
• “GO” - Building your own ChatBot with AWS Lex – May 7
Boaz Ziniman
• “And Beyond” - Amazon Sagemaker – May 8
Guy Ernest
Ready, Set, Go! And beyond - AI/ML webinar series in Hebrew

Amazon.com,1995

« Two Decades of Recommender Systems at
Amazon.com » (2017)
https://www.computer.org/csdl/mags/ic/2017/03/mic2017030012.html
G.D. Linden, J.A. Jacobi, and E.A. Benson,
Collaborative Recommendations Using Item-to-
Item Similarity Mappings, US Patent 6,266,649, to
Amazon.com, Patent and Trademark Office, 2001
(filed 1998).

Our mission
Put Machine Learning in the hands of
every developer and data scientist

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

More AI/ML is built on AWS than anywhere else

Application Services

Amazon Rekognition
Deep Learning-based image
analysis service

Amazon
Rekognition
Object and scene detection
Facial analysis
Face comparison
Celebrity recognition
Image moderation
Text in image
Image analysis service

Object & Scene Detection

Facial Analysis

NEW
Crowd Detection – up to 100 faces

Facial Search

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

Celebrity Recognition

NEW
Text in Image

Rekognition API example
aws rekognition detect-labels
--image '{"S3Object":{"Bucket":"demos.ziniman.com","Name":"photos/reko.jpg"}}'
{
"Labels": [
{
"Confidence": 99.14048767089844,
"Name": "Human"
},
{
"Confidence": 99.1404800415039,
"Name": "People"
},
{
"Confidence": 99.14048767089844,
"Name": "Person"
}……

Rekognition API example
aws rekognition detect-faces
--image '{"S3Object":{"Bucket":"demos.ziniman.com","Name":"photos/reko.jpg"}}'
--attributes "ALL”
{
"FaceDetails": [
{
....
"Gender": {
"Confidence": 99.9211654663086,
"Value": "Male"
},
"AgeRange": {
"High": 52,
"Low": 35
},
....

Marinus Analytics
Marinus Analytics provides law enforcement with
tools founded in artificial intelligence.
Traffic Jam, is a suite of tools for use by law
enforcement agencies on sex trafficking
investigations.
Before using Amazon Rekognition, their only
recourse was manual processing; this was time-
intensive or not possible.
Now, investigators are able to take effective action
by searching through millions of records in
seconds to find victims.
http://www.marinusanalytics.com/articles/2017/10
/17/amazon-rekognition-helps-marinus-analytics-
fight-human-trafficking

Amazon Rekognition Video
Deep Learning-based video
analysis service
NEWReady, Set, Go! And beyond - AI/ML webinar series in Hebrew

Video Analysis

City of Orlando
Real-time video analysis
”The City of Orlando is excited to work with Amazon to pilot the latest
in public safety software through a unique, first-of-it's-kind public-
private partnership. Through the pilot, Orlando will utilize Amazon
Rekognition Video and Amazon Kinesis Video Streams technology in a
way that will use existing City resources to provide real-time detection
and notification of persons-of-interests, further increasing public safety,
and operational efficiency opportunities for the City of Orlando and
other cities across the nation”.
John Mina, Police Chief, City of Orlando

Amazon Polly
Deep Learning-based text-to-
speech service

Amazon Polly
“Today in Seattle, WA
it’s 11°F”
Amazon Polly: Text In, Life-like Speech Out
52 voices across 25 languages
“Today in Seattle Washington
it’s 11 degrees Fahrenheit”

Amazon Polly
Amazon Polly: Text In, Life-like Speech Out
52 voices across 25 languages
“Today in Mumbai,
India it’s 32°C”
“Today in Mumbai, India it’s 32
degrees Celcius”

A Focus On Voice Quality & Pronunciation
1. Automatic, Accurate Text Processing
• “Today in Seattle, WA, it’s 11°F”

2. Intelligible and Easy to Understand

3. Add Semantic Meaning to Text

https://www.w3.org/TR/speech-synthesis/
<speak>
The spelling of my name is
<prosody rate='x-slow'>
<say-as interpret-as="characters">Boaz</say-as>
</prosody>
</speak>

3. Add Semantic Meaning to Text
• “Richard’s number is 2122341237“
<say-as interpret-as="telephone">

Voice Modification: Vocal Tract Length
<speak>
This is Brian without any voice modifications.
<amazon:effect vocal-tract-length="+15%"> Imagine now that I got bigger… </amazon:effect>
<amazon:effect vocal-tract-length="+25%"> Suppose that I got even bigger still… </amazon:effect>
Now let's go back and hear the effect when I go in the opposite direction.
<amazon:effect vocal-tract-length="-15%"> Can you tell that I'm getting smaller? </amazon:effect>
<amazon:effect vocal-tract-length="-25%"> Now I'm even smaller than before. </amazon:effect>
</speak>

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

Duolingo
• Duolingo is the most popular
language-learning platform and the
most downloaded education app in
the world, with more than 170
million users.
• They have run six A/B tests, testing
an Amazon Polly voice against a
voice from other TTS providers.
• For all of these experiments, the
winning condition was the Amazon
Polly voice
https://aws.amazon.com/blogs/machine-
learning/powering-language-learning-on-
duolingo-with-amazon-polly/

Amazon Translate
Neural Machine Translation Service
(Preview)

“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

Automatic translation
Real-time
translation
Powered by Deep
Learning
12 Language pairs
(more to come)
Language detection

Hotels.com
Machine Translation
At Hotels.com, we are committed to offering all of our customers
the most relevant and up to date information about their
destination. To achieve that, we operate 90 localized websites in
41 languages. We have more than 25M customer reviews and
more are coming in every day, making a great candidate for
machine translation. 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

Amazon Transcribe
Automatic speech recognition service
NEW
(Preview)

“Hello, this is Allan
speaking”
Amazon
Transcribe

Support for
telephony
audio
Timestamp
generation
Intelligent
punctuation
and formatting
Recognize
multiple
speakers
Custom
vocabulary
Multiple
languages

ringDNA
RingDNA is an end-to-end communications
platform for sales teams.
Hundreds of enterprise organizations use
RingDNA to 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

Amazon Comprehend
Natural Language Processing

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

Topic modeling
STORM
WORLD SERIES
AUSTRALIASTOCK
MARKET
WASHINGTON
HEALTH
CRISIS
MACHINE
LEARNING
LIBRARY OF
NEWS ARTICLES *
Amazon
Comprehend

Amazon Lex
Conversational Interfaces

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

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

Platform Services

Amazon EMR
ML on Amazon Elastic Map Reduce

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

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

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

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

Digital Globe
In the last 18 years DigitalGlobe has been
operating Earth imaging satellites, they
have collected over 100 PB of imagery.
There is a trade-off between how quickly
data can be accessed and how much it will
cost to store.
Working with the ML Lab, Digital Globe
built a predictive model that will reduce
cloud storage costs for their imagery
archive by 50%. http://blog.digitalglobe.com/industry/using-
machine-learning-to-save-money-on-cloud-data-
storage/
https://www.youtube.com/watch?v=mkKkSRIxU8M

Frameworks & Infrastructure

Amazon EC2 P3 Instances
T h e f a s t e s t , m o s t p o w e r f u l G P U i n s t a n c e s i n t h e c l o u d
• U p t o e i g h t N V I D I A T e s l a V 1 0 0 G P U s
• 1 P e t a F L O P s o f c o m p u t a t i o n a l p e r f o r m a n c e –
1 4 x b e t t e r t h a n P 2
• 3 0 0 G B / s G P U - t o - G P U c o m m u n i c a t i o n
( N V L i n k ) – 9 x b e t t e r t h a n P 2
• 1 6 G B G P U m e m o r y w i t h 9 0 0 G B / s e c p e a k G P U
m e m o r y b a n d w i d t h

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

AWS DeepLens
Deep Learning enabled video camera
for developers
NEW
(Pre-orders)

A new way to learn
Custom built for deep learning
Broad Framework Support
Deploy models from Amazon SageMaker
Integrated with AWS
Full programmable with AWS Lambda
AWS DeepLens
World’s first Deep Learning enabled video camera for developers

Object detection with AWS DeepLens

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

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

Thank you!
Boaz Ziniman – Technical Evangelist, AWS
@ziniman
boaz.ziniman.aws

Introduction to AI on AWS - AI/ML Hebrew Webinar

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