Artificial Intelligence & Machine Learning on AWS

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Steve Shirkey, ASEAN Solutions Architect
Nov 22nd, 2017
Building AI/ML Solutions on AWS

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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AI Technologies Will Be in Almost Every New
Software Product by 2020 – Gartner
“By 2020, AI will be a top five investment priority
for more than 30 percent of CIOs – Gartner
“The market for cognitive/AI solutions will experience
a compound annual growth rate (CAGR) of 55.1
percent…to more than $47 billion in 2020” – IDC

"We are solving problems with machine learning and artificial
intelligence that were in the realm of science fiction for the past
several decades"
- Jeff Bezos, 2017
Amazon and our customers are innovating to change what is
possible within industries or even creating entirely new markets

What is AI?
https://www.geospatialworld.net/blogs/difference-between-ai%EF%BB%BF-machine-learning-and-deep-learning/

What is AI?
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

Algorithms
Data
Programming
Models
GPUs &
Acceleration
The Advent of Deep Learning
image understanding
natural language
processing
speech recognition
autonomy

Artificial Intelligence
At Amazon (1995)

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

Alexa Voice Service
Empower your connected products with Alexa, Amazon’s intelligent
cloud-based voice service

Cloud-Based Intelligence
Alexa is always getting smarter with an ever-expanding list of supported
features, services, and third-party skills
Smart Home
Control
Timers and Alarms
Cloud Based
Intelligence
Shopping
Music
News and
Information
Custom
Skills

Personalized content
- Account access
- Track spending
- Check balances
- Pay bills
- Prevent fraud
Voice recognition
understands your requests
Natural speech responses
create conversation

Get Ready for Voice-
Enabled Lights; GE is
‘First’ with Alexa Inside
Huawei adds Amazon Alexa
to flagship Mate 9
smartphone
Volkswagen will be
integrating Amazon’s
Alexa into its cars
Ford brings Amazon Alexa to
its Sync 3 vehicles
LG's InstaView puts Alexa
in a fridge
Ubtech unveils Amazon
Alexa-powered Lynx robot
DISH Brings Amazon
Alexa Voice Control to
Hopper 3 DVR

echo look Machine learning rates and
recommends your best looks
Voice recognition understands
your requests
Image recognition sees what
you're wearing
Natural speech responses
create conversation

Systems see which items
have been taken or returned
Machine learning understands
in-store and purchase patterns
Combination of computer vision,
sensor fusion, and deep learning
Just Walk Out Technology

PrimeAir
Edge compute allows for real-time
decisions to be made in-vehicle, driven
by computer vision and deep learning
Sense and Avoid Technology

Running AI In Production on AWS Today

Recommendations & Ranking At Netflix
Personalized ranking,
page generation,
search, similarity, ratings
In 140 new countries,
simultaneously
Machine learning
understands similarity
Machine learning personalizes
rankings and recommendations

Managing over 10 million images from nearly 300,000
hotels to find photos that resonate with customers
Reduced ranking time from one week to one day
Crowd-sourced ratings from over 100,000
hotel training images, fine-tuned ratings using
TensorFlow on Tesla K80 GPUs on AWS
Uncovering images that
resonate with customers

Pinterest Lens
Image recognition finds
visually similar subjects
Image recognition finds
items and describes them
Pinterest Visual Search

Machine learning analyzes financial
transactions and predicts fraudulent
activity before it happens
On a given day, 100 fraud schemes appear, with
100 variations each. Machine learning models
are built for these specialized situations
Saves 400+ online merchants
about $1 million weekly

Artificial Intelligence requires a rich set of technologies and services
to support the growing demands for innovation.
Developers using our technologies may want to build at different
levels with fully managed application services, with development
platforms, with coding frameworks, and directly leveraging
specialized infrastructure

Vision Speech LanguageServices
Platforms
Frameworks
Infrastructure
Amazon ML Spark & EMR
Kinesis Batch ECS
AWS Deep Learning AMI
Apache
MXNet
TensorFlow
Caffe2
& Caffe
Theano Keras
Cognitive
Toolkit
Torch
GPU CPU IoT Mobile
Services: AI-Assisted Applications
Platforms: AI Model Creation
Frameworks: Deep Learning
Infrastructure: Scale and Power

Services: Pre-built models to be consumed as APIs
Front-End team
Data Engineering team
Analysts / DS team
DevOps team
Business Problem
Data
ML Model
ML Application

Front-End team
Data Engineering team
Analysts / DS team
DevOps team
Business Problem
Data
ML Model
ML Application
Heavy Lifting by AWS
Services: Pre-built models to be consumed as APIs

Platforms
Frameworks
Infrastructure
Kinesis Batch ECS
Apache
MXNet
TensorFlow
Caffe2
& Caffe
Theano Keras
Cognitive
Toolkit
Torch
GPU CPU IoT Mobile
Platforms: AI Model Creation

Amazon AI/ML Services are designed for ease-of-use
Amazon data
science expertise
Amazon training
data
Amazon models
and learning
Accessibility via
API
Foundational components of Amazon AI/ML services
PollyLex Rekognition
Scalable, trained, and continuously optimized services
that do not require extensive AI/ML expertise

Amazon Rekognition
Image Recognition and Analysis powered by Deep Learning
allows search, verification and organization millions of
images
Easy to use Batch Analysis Real-time Analysis Continually Improving Low Cost
Potential Use Cases
Searchable Image Library Detect Inappropriate Content in Images
Face-based User Verification Sentiment Analysis
Facial Recognition Celebrity Identification

Amazon Rekognition
Extract rich metadata from visual content
Object and Scene
Detection
Facial
Analysis
Face
Comparison
Facial
Recognition
Celebrity
Recognition
Image
Moderation

Amazon Rekognition Features
• Object & Scene Detection
Photo-sharing apps can power smart searches and
quickly find cherished memories, such as weddings,
hiking, or sunsets
• Facial Analysis
Retail businesses can understand the demographics
and sentiment of in-store customers
• Face Comparison
Hotels & hospitality businesses can provide
seamless access for guests and VIPs
• Facial Recognition
Public safety teams can leverage face collections to
automate tracking of persons of interest

Object & Scene Detection
Object and scene detection makes it easy for you to add features that
search, filter, and curate large image libraries.
Identify objects and scenes and provide confidence scores
DetectLabels
Flower
Arrangement
Chair
Coffee Table
Living Room Indoors
Furniture
Cushion
Vase
Maple
Villa
Plan
t
Garden
Water
Swimming Pool
Tree
Potted Plant
Backyard
Pati
o

Facial Analysis
Analyze facial characteristics in multiple dimensions
DetectFaces
Image Quality
Facial Landmarks
Demographic Data Emotion Expressed
General Attributes
Facial Pose
Brightness 23.6%
Sharpness 99.9%
EyeLeft,EyeRight,Nose
RightPupil,LeftPupil
MouthRight,LeftEyeBrowUp
Bounding Box...
Age Range 29-45
Gender: Male 96.5%
Happy 83.8%
Surprised 0.65%
Smile:True 23.6%
EyesOpen:True 99.8%
Beard:True 99.5%
Mustache:True 99.9%...
Pitch 1.446
Roll 5.725
Yaw 4.383

Face Comparison
Measure the likelihood that faces are of the same person
Similarity 93% Similarity 0%
CompareFaces

Facial Recognition
Find similar faces in a large collection of images
SearchFacesByImage
Search
Index
Collection

Celebrity Recognition & Image Moderation
Newly released Rekognition features
Detect explicit and suggestive contentRecognize thousands of famous individuals
DetectModerationLabelsRecognizeCelebrities

Interfacing with Rekognition
• S3 input for API calls - max image size of 15MB
• 5MB limit for non-S3 (Base64 encoded) API calls
• Minimum image resolution (x or y) of 80 pixels
• Image data supported in PNG or JPG format
• Max number of faces in a single face collection is 1
million
• The max matching faces the search API returns is 4096
• Size of face should occupy 5%+ of image for detection
• Collections are for faces!
Optimizing your input & requests for best performance
…
Use Amazon CloudWatch to observe & issue alerts on Rekognition metrics

Amazon Rekognition Customers

• Built in 3 weeks
• Indexed against 99,000 people
• Index created in one day
• Saved ~9,000 hours a year in manual
curation costs
• Live video with frame sampling
Automating Footage Tagging with
Amazon Rekognition
Previously, only about half of all footage was indexed due to the
immense time requirements required by manual processes

https://console.aws.amazon.com/rekognition/home

Demo: http://amzn.to/takeselfie

Advent of Conversational Interactions
1st Gen:
Punch Cards & Memory Registers
2nd Gen:
Pointers & Sliders
3nd Gen:
Conversational Interfaces

The Advent Of Conversational Interactions
1st Gen
Machine-oriented
interactions

2nd Gen
Control-oriented
& translated
1st Gen
Machine-oriented
interactions

3rd Gen
Intent-oriented
2nd Gen
Control-oriented
& translated
1st Gen
Machine-oriented
interactions

Conversational AI use cases
Informational Bots
Chatbots for everyday consumer requests
Application Bots
Build powerful interfaces to mobile applications
• News updates
• Weather
information
• Game scores ….
• Book tickets
• Order food
• Manage bank accounts ….
Enterprise Productivity Bots
Streamline enterprise work activities and improve efficiencies
• Check sales numbers
• Marketing performance
• Inventory status ….
Internet of Things (IoT) Bots
Enable conversational interfaces for device interactions
• Wearables
• Appliances
• Auto ….

Developer challenges for Conversational AI
Conversational interfaces need to combine a large number of
sophisticated algorithms and technologies
Speech
Recognition Language
Understanding
Business Logic
Disparate Systems
Authentication
Messaging
platforms
Scale Testing
Security
Availability
Mobile

Amazon Lex
Conversational interfaces for your applications, powered by the
same Natural Language Understanding (NLU) & Automatic
Speech Recognition (ASR) models as Alexa
Integrated
development in
AWS console
Trigger AWS
Lambda
functions
Multi-step
conversations
Continually
improving ASR &
NLU models
Enterprise
connectors
Fully Managed
Potential Use Cases
Appointment Booking Customer Support
Informational Services Access Enterprise Data
Internet of Things (IoT)

Text and speech language understanding
Speech
Recognition
Natural Language
Understanding
Powered by the same deep learning technology as Alexa

Amazon Lex
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

“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 April 19th
Check Out April 21st
“Your hotel is booked for
April 19th”
Amazon Polly
Confirmation: “Your hotel
is booked for Nov 30th”
“Can I go ahead
with the booking?
a
in

Designed for builders
Efficient and intuitive tools to build conversations

Multi-platform
Mobile Messaging
Platforms
Web IoT
SDKs: iOS & Android
Mobile Hub
Facebook, Twilio
SMS and Slack
SDKs: Java, JavaScript,
Python, CLI, .NET, Ruby on
Rails, PHP, Go, and CPP
Integrated with AWS
IoT via AWS Lambda
Build once and deploy to multiple platforms

Messaging platforms
Facebook Slack Twilio SMS

Rich message formatting
I would like to rent a car
Sure. What type of car are
you looking for?
Compact
• Formatted for messaging platforms
• Multiple cards supported
• Preview capability
• Test in console

Amazon Lex Customers

Unlimited
Replays
Returns an MP3
or audio stream
Lightning Fast
Response
Fully Managed and
Low Cost
Amazon Polly
Turn text into lifelike speech using deep learning
technologies to synthesize speech that sounds like a human
voice
Potential Use Cases
Content Creation Education & E-learning
Mobile & Desktop Applications Customer Contact Center
Internet of Things (IoT) Accessibility

Amazon Polly: life-like speech service
Converts text
to life-like speech
52 voices 25 languages Low latency,
real time
Fully managed

Amazon Polly
“The temperature
in SGP is 33°C”
“The temperature
in Singapore is 33 degrees
Celsius”
Amazon Polly: Semantics In, Life-like Speech Out

Amazon Polly Demo
Natural sounding speech
A subjective measure of how close TTS output is to human speech.
Accurate text processing
Ability of the system to interpret common text formats such as abbreviations, numerical
sequences, homographs etc.
12°C is the same as, 53.6°F. If you enjoyed this summit be sure to go to our conference "Re:Invent"
in Las Vegas NV
"We live for the music", live from the Madison Square Garden.
Highly intelligibile
A measure of how comprehensible speech is.
”Peter Piper picked a peck of pickled peppers.”

Amazon Polly Customers

High quality language education using Amazon Polly Text-to-Speech (TTS)
>170M users: Most popular language-learning platform
Each lesson targets different linguistic skills and concepts.
Amazon Polly has been the A/B winner against their previous TTS providers in all
six voice tests they use to evaluate impact to their business.

Platforms
Frameworks
Infrastructure
Kinesis Batch ECS
Apache
MXNet
TensorFlow
Caffe2
& Caffe
Theano Keras
Cognitive
Toolkit
Torch
GPU CPU IoT Mobile

Managed machine learning service
Easy to use, managed machine learning service
built for developers
• Binary and Multiple Classification
Sentiment analysis – Do people like my new
product?
• Linear Regression
Trend prediction – How much revenue next
month?
Use data in Amazon S3, Amazon RDS or Amazon
Redshift
Provides batch or real-time predictions
Amazon Machine
Learning

Machine Learning Pipeline
Collected Data
Validation data Test dataTraining data
Model training Model validation Final predictions
Process Data
Feature Extraction
Model Training
Model Validation
Model Testing

Training Data Set
Customer information like demographics, location, usage, social cluster etc.
Customer Churn Prediction

Create Data Source

Define a Schema

Define Target in Data

Create a Machine Learning Model

Model Creation and Evaluation

Publish the Model for Execution

”
“
Fraud.net Uses AWS to Quickly, Easily Detect Online Fraud
Fraud.net is the world’s leading crowdsourced
fraud prevention platform.
Amazon Machine Learning
helps us reduce complexity
and make sense of emerging
fraud patterns.
• Needed to build and train a larger number of more
targeted and precise machine-learning models
• Uses Amazon Machine Learning to provide more
than 20 machine-learning models
• Easily builds and trains machine-learning models to
effectively detect online payment fraud
• Reduces complexity and makes sense of emerging
fraud patterns
• Saves clients $1 million weekly by helping them
detect and prevent fraud
Oliver Clark
CTO,
Fraud.net
”
“

If you want to go deeper
Continuum of ML Solutions
Amazon ML
service
EMR service with
Spark ML
• Limited ML Options
• Easy to Train
• Easy to Evaluate
• Quick to deploy
• Comprehensive ML Options
• Significant work to Train
• No support for evaluation
• Additional work to deploy
• Potentially more scalable
• More customizable

Build and develop machine learning models
MLlib
Amazon EMR
with Spark ML

Platforms
Frameworks
Infrastructure
Kinesis Batch ECS
Apache
MXNet
TensorFlow
Caffe2
& Caffe
Theano Keras
Cognitive
Toolkit
Torch
GPU CPU IoT Mobile

AWS Deep Learning AMIs ready for you
Quickly launch Amazon EC2 instances, pre-installed with popular
deep learning frameworks to train sophisticated, custom AI models,
experiment with new algorithms, or to learn new skills and
techniques.
KERAS
More TensorFlow
applications are run on
AWS than any other
platform
Open source framework,
including convolutional
neural networks (CNNs)
and long short-term
memory (LSTMs)
High level API that is
user-friendly for a
broader base, to
increase ease of use for
TensorFlow and others

Support for all major frameworks
Apache MXNet
Torch
Cognitive
Toolkit
Keras
Theano
Caffe2
& Caffe
TensorFlow
Amazon EC2
AnacondaIntel MKL
Nvidia CUDA &
cuDNN
Python2 &
Python3

Platforms
Frameworks
Infrastructure
Kinesis Batch ECS
Apache
MXNet
TensorFlow
Caffe2
& Caffe
Theano Keras
Cognitive
Toolkit
Torch
GPU CPU IoT Mobile

Model
Training
Infrastructure to support model build and deploy

Model
Training
Inference
in the Cloud

Model
Training
Inference
in the Cloud
Inference
at the Edge

Deep Learning Training on GPU
MXNet training on EC2 P2
instances:
§ Training of a popular image
analysis algorithm, Inception
v3, using MXNet and running
on P2 instances
§ Scaling efficiency of 85%

NVIDIA GPU Generation
NVIDIA Roadmap
from GTC 2017

P3: Next Generation NVIDIA GPUs
Core of the next AWS GPU instance family
Custom built for artificial intelligence
Train in hours, not days
NVIDIA Volta
P3

Accelerated Computing Instances
P3: NVIDIA Volta GPU Compute Instance
• Up to 8 NVIDIA Tesla V100 GPUs in a single instance
• Use cases include deep learning, HPC simulations, financial computing, batch rendering
G3: NVIDIA Maxwell GPU Graphics (and Compute) Instance
• Up to 4 NVIDIA M60 GPUs, with GRID Virtual Workstation features and licenses
• Use-cases include 3D rendering/visualizations, graphics-intensive remote workstations, video
encoding, and virtual reality, and single-precision HPC
P2: NVIDIA Kepler GPU Compute Instance
• Up to 16 NVIDIA GK210 (8 X K80) GPUs in a single instance
• Use cases include deep learning, HPC simulations, financial computing, batch rendering
F1: FPGA instance
• Up to 8 Xilinx Virtex® UltraScale+™ VU9P FPGAs in a single instance
• Use cases include genomics, accelerated search, video and image processing, deep learning
inference
G3
P2
F1
P3

Fully integrate AI services into your applications, leveraging your
existing business data assets, your streaming data, your data lakes,
and your operational systems
Use blueprints for what has been done to achieve what is possible...

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Artificial Intelligence & Machine Learning on AWS

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