Introduction to AI services for Developers - Builders Day Israel

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Builders’ Day
Jerusalem
Introduction to AI services for Developers
Boaz Ziniman – Technical Evangelist, AWS
@ziniman
boaz.ziniman.aws

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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
Ready, Set, Go! And beyond - AI/ML webinar series in Hebrew

Machine Learning On AWS Today

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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

AVX 512
72 vCPUs
“Skylake”
144 GiB memory
C5
12 Gbps to EBS
2X vCPUs
2X performance
3X throughput
2.4X memory
C4
36 vCPUs
“Haswell”
4 Gbps to EBS
60 GiB memory
C5 with Intel® Xeon® Scalable Processor
C5: Next Generation
Compute-Optimized Instances
with Intel® Xeon® Scalable
Processor
AWS Compute optimized
instances support the new Intel®
AVX-512 advanced instruction
set, enabling you to more
efficiently run vector processing
workloads with single and double
floating point precision, such as
AI/machine learning or video
processing.

EU (Ireland) Region Linux On Demand Pricing
vCPU ECU Memory (GiB) Instance Storage (GB) Linux/UNIX Usage
CPU c5.large 2 8 4 EBS Only $0.096 per Hour
c5.xlarge 4 16 8 EBS Only $0.192 per Hour
c5.2xlarge 8 31 16 EBS Only $0.384 per Hour
GPU p2.xlarge 4 12 61 EBS Only $0.972 per Hour
p2.8xlarge 32 94 488 EBS Only $7.776 per Hour
p3.2xlarge 8 23.5 61 EBS Only $3.305 per Hour
Source - https://aws.amazon.com/ec2/pricing/on-demand/?refid=em_67469 As of 25th April 2018

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

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

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Platform Services

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

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Application Services

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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

Smiling?
Facial Analysis
(Deep) Learning from a Masterpiece
http://bit.ly/MonaLisaAI

NEWCrowd 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

NEWText in Image

Rekognition API example
boazz: ~/ 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
boazz: ~/ 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

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Amazon Rekognition Video
Deep Learning-based video
analysis service
NEW

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

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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/

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Amazon Translate
Neural Machine Translation Service
NEW

“Hello, what’s up? Do
you want to go see a
movie tonight?”
Amazon Translate
Natural and fluent language translation
" Hallo, was ist los? Willst
du heute Abend einen
Film sehen? "
Amazon
Translate

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

Hotels.com
Matt Fryer, VP and Chief Data Science Officer, 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.

Demo

Polly
def speak(text_string, voice="Joanna"):
try:
# Request speech synthesis
response = polly.synthesize_speech(Text=text_string,
TextType="text", OutputFormat="pcm", VoiceId=voice)
except (BotoCoreError, ClientError) as error:
# The service returned an error, exit gracefully
print(error)
exit(-1)
# Access the audio stream from the response
if "AudioStream" in response:
stream = pya.open(format=pya.get_format_from_width(width=2), channels=1, rate=16000,
output=True)
stream.write(response['AudioStream'].read())
sleep(1)
stream.stop_stream()
stream.close()
else:
# The response didn't contain audio data, return False
print("Could not stream audio")
return(False)

Rekognition
def reko_detect_labels(image_bytes):
response = reko.detect_labels(
Image={
'Bytes': image_bytes
},
MaxLabels=8,
MinConfidence=60
)
return response
def reko_detect_faces(image_bytes):
response = reko.detect_faces(
Image={
'Bytes': image_bytes
},
Attributes=['ALL']
)
return response

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Amazon Transcribe
Automatic speech recognition service
NEW

“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

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Amazon Comprehend
Natural Language Processing
NEW

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

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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

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

Builders’ Day
Jerusalem
Thank You!
Boaz Ziniman – Technical Evangelist, AWS
@ziniman
boaz.ziniman.aws

Introduction to AI services for Developers - Builders Day Israel

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