Building the Organization of the Future: Leveraging Machine Learning

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Alex Coqueiro
Public Sector Solutions Architect Team
Amazon Web Services
September, 2018
Building the Organization of the Future:
Leveraging Machine Learning

A Flywheel For Building Organizations
Users Better Products
More Data Better Analytics
More

Machine Learning will empower
the business
Eventually everything connects
IoT/Mobile/Cameras/APIs/Datalake
Large amounts of data will be
processed in real-time

At Amazon, we’ve been making investments
in ML for the last 20 years…
Search &
Discovery
Fulfilment
& Logistics
Existing
Products
New
Initiatives

Tens Of Thousands Of Customers Running
Machine Learning On AWS

Recognize the discrete
anatomical structures of
the eye, such as the
retina, cornea or optic
nerve to identify diabetic
retinopathy
(leading cause of
blindness)

“The future is here.
It’s just not widely distributed yet.”
William Gibson
American-Canadian fiction writer

Put machine learning in the
hands of every developer
and data scientist
ML @ AWS:
O U R M I S S I O N

AWS Machine Learning Stack
Frameworks &
Infrastructure
GPU MobileCPU IoT (AWS Greengrass)
Platform
Services
Application
Services
Amazon SageMaker
LanguageVision
Amazon
Rekognition
Image
Amazon
Rekognition
Video
Amazon
Translate
Amazon
Comprehend
Amazon
Lex
Speech
Amazon
Polly
Amazon
Transcribe
KERAS Gluon
AWS DeepLens

AWS has shipped over 100 major
new features and new services for
machine learning since re:Invent
2017
A m a z o n R e k o g n i t i o n I m a g e a n d V i d e o l a u n c h i n U S E a s t ( O h i o ) A d v a n c e d I n d e x i n g S u p p o r t A m a z o n L e x r e g i o n s u p p o r t A m a z o n L e x : D e f a u l t R e s p o n s e s
A m a z o n L e x : D T M F S u p p o r t
A m a z o n P o l l y W o r d P r e s s p l u g i n i n t e g r a t e s T r a n s l a t e A m a z o n R e k o g n i t i o n I m a g e a n d V i d e o l a u n c h i n T o k y o a n d S y d n e y A m a z o n T r a n s c r i b e - G A A m a z o n T r a n s l a t e - G A A n o m a l y D e t e c t i o n ( R a n d o m C u t F o r e s t ) A l g o r i t h m
A u t o m a t e r o l e c r e a t i o n d u r i n g s e t u p A u t o m a t i c M o d e l T u n i n g A u t o s c a l i n g c o n s o l e A W S D e e p L e n s d e v i c e s a v a i l a b l e f o r s a l e B l a z i n g T e x t A l g o r i t h m B u i l t - i n A l g o r i t h m s P i p e M o d e S u p p o r t C a f f e t o M X N e t c o d e t r a n s l a t o r
C h a i n e r p r e - b u i l t c o n t a i n e r C l o u d F o r m a t i o n s u p p o r t C l o u d T r a i l i n t e g r a t i o n f o r a u d i t l o g s C U D A 9 . 0 a n d 9 . 1 + c u D N N 7 . 1 . 4 + G P U D r i v e r 3 9 0 . 4 6 + N C C L 2 . 2 . 1 3 u p g r a d e C u s t o m e r V P C s u p p o r t f o r t r a i n i n g a n d h o s t i n g D C A : A W S D e e p L e a r n i n g A M I i n D C A
D e e p A R a l g o r i t h m D e e p L e a r n i n g A M I i n G o v C l o u d D e e p L e a r n i n g A M I : A d d C h a i n e r , M X N e t 1 . 1 s u p p o r t ( i n c l . B J S ) D e e p L e a r n i n g A M I : E C 2 O p t i m i z e d B i n a r i e s f o r T e n s o r F l o w D e e p L e a r n i n g A M I : O p t i m i z e d C h a i n e r 4 , C N T K 2 . 5
D e e p L e a r n i n g A M I : R e f r e s h f o r E C 2 o p t i m i z e d T e n s o r F l o w 1 . 8 D e e p L e a r n i n g A M I : R e f r e s h w i t h T F 1 . 5 a n d M X N e t 1 . 1 D e e p L e a r n i n g A M I : R e g i o n a l E x p a n s i o n i n 5 p u b l i c r e g i o n s : U S W e s t ( C a l i f o r n i a ) , C a n a d a , S a o P a u l o , L o n d o n , a n d P a r i s
D e e p L e a r n i n g A M I : R e l e a s e l a t e s t D L A M I v e r s i o n i n B J S D L A M I l a u n c h i n B J S a n d F R A , B O M , S I N
D e e p L e a r n i n g A M I : T e n s o r F l o w m u l t i - g p u o p t i m i z a t i o n s u s i n g H o r o v o d , N C C L a n d O p e n M P ID e e p L e a r n i n g A M I : T F 1 . 5 R C 0 , M M S , T F S e r v i n g , T e n s o r B o a r d ( i n c l u d i n g B J S )F P 1 6 s u p p o r t
D L A M I r e f r e s h w i t h M X N e t 1 . 0 , T F f o r C U D A 9 a n d P y T o r c h f o r C U D A 9
D e e p L e n s i n t e g r a t i o n w i t h A m a z o n K i n e s i s v i d e o s t r e a m E x p o r t / I m p o r t A m a z o n L e x b o t s c h e m a
F a c e R e c o g n i t i o n v 3 l a u n c h G D P R C o m p l i a n c e G r a d i e n t C o m p r e s s i o n
H I P A A c o m p l i a n c e
I m p o r t f r o m S a g e M a k e r t o D e e p L e n s I n c r e a s e C h a r a c t e r L i m i t I n t e r n e t - f r e e n o t e b o o k i n s t a n c e s K M S s u p p o r t f o r t r a i n i n g a n d h o s t i n g L i n e a r L e a r n e r I m p r o v e m e n t s - E a r l y S t o p p i n g , N e w L o s s F u n c t i o n s , a n d C l a s s W e i g h t s m l . p 3 . 2 x l a r g e n o t e b o o k i n s t a n c e s
M o d e l O p t i m i z e r M o r e i n s t a n c e t y p e s s u p p o r t M X N e t M o d e l S e r v e r M X N e t M o d e l S e r v e r M X N e t M o d e l S e r v e r v 0 . 2 M X N e t M o d e l S e r v e r v 0 . 3 M X N e t v 1 . 0 r e l e a s e M X N e t v 2 . 0 r e l e a s e n b e x a m p l e s u p p o r t i n S a g e M a k e r n o t e b o o k i n s t a n c e s
N C C L s u p p o r t
N E R , K e y p h r a s e , S e n t i m e n t B a t c h N e w B r e a t h S S M L t a g N e w F r e n c h V o i c e ( f e m a l e ) N e w P h o n a t i o n S S M L t a g N o t e b o o k b o o t s t r a p s c r i p t O N N X v 1 . 0 a n n o u n c e m e n t P C I D S S C o m p l i a n c e P o l l y i n G o v C l o u d P o l l y v o i c e s i n A l e x a S k i l l s
P r i v a t e L i n k s u p p o r t f o r S a g e M a k e r i n f e r e n c i n g A P I s P y T o r c h p r e - b u i l t c o n t a i n e r R e f r e s h D L A M I i n D C A R e g i o n e x p a n s i o n t o F R A R e g i o n e x p a n s i o n t o N R T R e g i o n e x p a n s i o n t o S y d n e y R e k o g n i t i o n H I P A A s u p p o r t R e k o g n i t i o n V i d e o i n A W S G o v C l o u d
S a g e M a k e r r e g i o n e x p a n s i o n t o I C N S t o r e t r a n s c r i p t i o n o u t p u t i n c u s t o m e r S 3 b u c k e t s S u p p o r t f o r K e r a s 2 . 0 S u p p o r t G l u o n m o d e l s T a g - b a s e d a c c e s s c o n t r o l T e n s o r F l o w 1 . 5 , M X N e t 1 . 0 , a n d C U D A 9 S u p p o r t T e n s o r F l o w 1 . 6 a n d M X N e t 1 . 1 C o n t a i n e r s
T e n s o r F l o w 1 . 7 C o n t a i n e r s T e n s o r F l o w 1 . 8 C o n t a i n e r T e n s o r f l o w a n d C a f f e s u p p o r t T e n s o r F l o w a n d M X N e t C o n t a i n e r s - O p e n S o u r c i n g a n d L o c a l M o d e T r a i n i n g J o b c l o n i n g i n c o n s o l e T r a n s c r i b e i n t e g r a t i o n w i t h C l o u d T r a i l T r a n s c r i b e i n t e g r a t i o n w i t h C l o u d W a t c h
A m a z o n P o l l y W o r d P r e s s P l u g i n
T r a n s l a t e - - a d d t o P o l l y W o r d P r e s s p l u g i n T r a n s l a t e - - G D P R c o m p l i a n c e
T r a n s l a t e - - G o v C l o u d T r a n s l a t e - - M o b i l e S D K
X G B o o s t I n s t a n c e W e i g h t s

“If we want machines to think, we need to
teach them to see”
Fei-Fei Li
Professor at Stanford University

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Image Detection
One face detected
Age Variation: 0-2 years old
10 faces detected Person 99.2%
Snowboarding 98.1%

Deep learning-based image recognition service
Search, verify, and organize millions of images
Object and
scene
detection
Facial
analysis
Text in
image
Face search
and match
Celebrity
recognition
Unsafe
image
detection
Amazon Rekognition

Face Comparison
Measure the likelihood that faces in two images are of
the same person
• Add face verification to
applications and devices
• Extend physical security
controls
• Provide guest access to
VIP-only facilities
• Verify users for online
exams and polls

Amazon Rekognition API
var compareParams = {
SimilarityThreshold: 90,
SourceImage: { ... },
TargetImage: { ... }
}};
rekognition.compareFaces(compareParams, function(err, data) {
if (err) {
console.log(err, err.stack); // an error occurred
} else {
if (data.FaceMatches.length > 0) {
//get item in dynamo
console.log("Similarity: " + data.FaceMatches[0].Similarity);
dynamodb.getItem(paramsItem, function(err, data) { ... }
}
});

Deep Learning – Neural Network
 Output
Neural Network
Input
Hidden layers
Computing systems inspired by the biological neural networks
which learn to do tasks by considering examples, generally
without task-specific programming

Deep Learning – Neural Network

A managed service
that provides a quick and easy way for
data scientists and developers to get
ML models from idea to production.

Amazon’s fast, scalable algorithms
Idiomatic, distributed TensorFlow & MXNet
Bring your own algorithm
Hyperparameter optimization
UX HostingTraining
Amazon SageMaker components

… or Apache Spark
through EMR and
the Amazon
SageMaker Spark
SDK...
UX
Use Amazon
SageMaker‘s
hosted Notebook
Instances...
... or Amazon
SageMaker‘s
Console for a point
and click
experience...
... or your own
device (EC2,
laptop, etc.)

Zero setup
Training
Streaming datasets
+ distributed
compute
Docker / ECS Deploy trained models
locally or to Amazon
SageMaker, AWS
Greengrass, AWS
DeepLens

One step
deployment
Low latency, high
throughput, and
high reliability
A/B testing Use your own
model
Hosting

XGBoost, FM,
Linear, and
Forecasting for
supervised
learning
Kmeans, PCA,
and BlazingText
(Word2Vec) for
clustering and
pre-processing
Image
classification with
convolutional
neural networks
LDA and
NTM for topic
modeling,
seq2seq for
translation
Built-in algorithms
Random Cut
Forest for
anomaly
detection

… explore and
refine models in a
single Notebook
Instance
TensorFlow & MXNet containers
… deploy to
production
Sample your
data…
Use the same code
to train on the full
dataset in a cluster
of GPU
instances…

Bring your own algorithm
... add algorithm
code to a Docker
container...
Pick your preferred
framework...
... publish to ECS

Hyperparameter Optimization
Run a large set of training
jobs with varying
hyperparameters...
... and search the
hyperparameter space for
improved accuracy.

Amazon Polly: Life-like Speech Service
Converts text
to life-like
speech
53 voices 25 languages Low latency,
real time
Fully managed

TEXT
Market grew by > 20%.
WORDSPHONEMES
{
{
{
{
{
ˈtwɛn.ti
pɚ.ˈsɛnt
ˈmɑɹ.kət ˈgɹu baɪ ˈmoʊɹ
ˈðæn
PROSODY CONTOURUNIT SELECTION AND ADAPTATION
TEXT PROCESSING
PROSODY MODIFICATIONSTREAMING
Market grew by more
than
twenty
percent
Speech units
inventory

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Example of API Usage to Convert Text into Voice
# Invoking Polly Session
polly = Session().client("polly")
#Synthesize Speech
response = polly.synthesize_speech(
Text=data,
TextType="ssml",
OutputFormat="mp3",
VoiceId="Vitoria”
)

Converting Text to Life-Like Speech
# Invoke Polly client
polly = Session().client("polly")
#Voice synthesize
response = polly.synthesize_speech(
Text=data,
TextType="ssml",
OutputFormat="mp3",
VoiceId="Vitoria”
)

Invoking Models
# Library Importing
import mxnet as mx
import numpy as np
# Model loading
sym, arg_params, aux_params = load_model(f_symbol_file.name, f_params_file.name)
# Parameters
mod = mx.mod.Module(symbol=sym)
mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])
mod.set_params(parameters, auxiliar_parameters)
# Model execution
labels = predict(url, mod, synsets)
https://aws.amazon.com/blogs/compute/seamlessly-scale-predictions-with-aws-lambda-and-mxnet/

REAL-TIME TRANSLATION POWERED BY DEEP
LEARNING
12 LANGUAGE PAIRS
(more to come)
LANGUAGE DETECTION
Amazon Translate: Neural Machine Translation

Amazon Comprehend: Natural Language
Processing
Sentiment Entities LanguagesKey phrases Topic modeling
Powered By Deep Learning

Expanding it into Virtual Reality (VR) and
Augumented Reality (AR)

Join us for our first-ever Amazon Web Services Summit
in Ottawa on October 29, 2018
15 sessions featuring various
management and technical
topics
Connect with AWS & our
Canadian public sector partners
in the Solutions Expo
Meet and mingle with other public
sector customers from government,
education, and nonprofits
Register today!
aws.amazon.com/summits/ottawa-public-sector

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