AWS AI Overview - NTU EEE...Amazon Rekognition Deep learning-based image recognition service Search,...
Transcript of AWS AI Overview - NTU EEE...Amazon Rekognition Deep learning-based image recognition service Search,...
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AWS AI Overview
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Topics
1. AI and Big Data Analytics
2. Amazon AI Offerings
3. AI Engines
4. AI Platforms
5. AI Services
6. Questions
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AI and Big Data Analytics
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More Users
More Data
A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
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A Flywheel For Data
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
More Users
More Data Better Analytics
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
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A Flywheel For Data
Machine Learning
Deep Learning
AI
More Users Better Products
More Data Better Analytics
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
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Machine Learning &
Artificial Intelligence
Big Data Analytics
More Users Better Products
More Data Better Analytics
A Flywheel For Data
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AI @ Amazon
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Artificial Intelligence
At Amazon (1995)
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Amazon AIIntelligent Services Powered By Deep Learning
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AWS AI Suite
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Amazon AI: New Deep Learning Services
Life-like Speech
Polly LexConversational
Engine
RekognitionImage Analysis
Deep Learning
FrameworksMXNet, TensorFlow,
Theano, Caffe, Torch
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DIY Deep Learningfor Custom Models
AI EnabledManaged APIServices
Amazon AI: New Deep Learning Services
Polly LexRekognition
Deep Learning
FrameworksMXNet, TensorFlow, Theano, Caffe, Torch
CO
NT
RO
LU
SA
BIL
ITY
&
SIM
PL
ICIT
Y
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One-Click GPU
Deep Learning
AWS Deep Learning AMI
Up to~40k CUDA cores
MXNet
TensorFlow
Theano
Caffe
Torch
Pre-configured CUDA drivers
Anaconda, Python3
+ CloudFormation template
+ Container Image
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MXNet: Scalable Deep Learning Framework
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Demo
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Amazon AIAI Services
Life-like Speech
Polly LexConversational
Engine
RekognitionImage Analysis
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Amazon Polly
Life-like Speech
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Converts text
to life-like speech
47 voices 24 languages Low latency,
real time
Fully managed
Polly: Life-like Speech Service
Voice Quality & Pronunciation
1. Automatic, Accurate Text Processing
2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
4. Customized Pronunciation
Articles and Blogs
Training Material
Chatbots (Lex)
Public Announcements
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Demo
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Amazon Rekognition
Image Analysis
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Images – explosive growth trends
Source: InfoTrends Worldwide Consumer Photos Captured and Stored.
2013 -2017 prepared for Mylio.
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Images – explosive growth trends
Amazon S3 – Images Stored
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Amazon RekognitionDeep learning-based image recognition service
Search, verify, and organize millions of images
Object and Scene
DetectionFacial
Analysis
Face
Comparison
Facial
Recognition
Integrated with S3, Lambda, Polly, Lex
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Object and Scene Detection
Generate labels for thousands of objects, scenes, and
concepts, each with a confidence score
• Search, filter, and curate image libraries
• Smart searches for user generated content
• Photo, travel, real estate, vacation rental applications
Maple
Plant
Villa
Garden
Water
Swimming Pool
Tree
Potted Plant
Backyard
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Facial Analysis
Locate faces within images and analyze face attributes to
detect emotion, pose, facial landmarks, and features
• Avoid faces when cropping images and overlaying ads
• Capture user demographics and sentiment
• Recommend the best photos
• Improve online dating match recommendations
• Dynamic, personalized ads
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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
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Facial Recognition
Identify people in images by finding the closest match for an
input face image against a collection of stored face vectors
• Add friend tagging to social and messaging apps
• Assist public safety officers find missing persons
• Identify employees as they access sensitive locations
• Identify celebrities in historical image archives
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Demo
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Amazon Lex
Conversational
Engine
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Advent of Conversational Interactions
1st Gen:
Punch Cards & Memory Registers
2nd Gen:
Pointers & Sliders
3nd Gen:
Conversational Interfaces
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Developer Challenges
Speech
Recognition Language
Understanding
Business Logic
Disparate
Systems
Authentication
Messaging
platforms
Scale Testing
Security
Availability
Mobile
Conversational interfaces need to combine a large number of
sophisticated algorithms and technologies
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Amazon Lex - Features
Text and Speech language understanding: Powered by the same technology as
Alexa
Enterprise SaaS Connectors: Connect to enterprise systems
Deployment to chat services
Designed for Builders: Efficient and intuitive tools to build
conversations; scales automatically
Versioning and alias support
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Enterprise Connectors with Mobile Hub
Amazon Lex
Mobile App
Mobile Hub
SaaS Connector
Amazon API
Gateway
AWS
Lambda
1: Understand
user intent
Amazon API
Gateway
AWS
Lambda
3: Translate
REST response
into natural
languageMobile Hub
Custom Connector
2: Invoke a SaaS
application or an existing
business application
Business
Application
Firewall
User Input
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Amazon Lex – Use Cases
Informational BotsChatbots for everyday consumer requests
Application BotsBuild powerful interfaces to mobile applications
• News updates
• Weather information
• Game scores ….
• Book tickets
• Order food
• Manage bank accounts ….
Enterprise Productivity BotsStreamline enterprise work activities and improve efficiencies
• Check sales numbers
• Marketing performance
• Inventory status ….
Internet of Things (IoT) BotsEnable conversational interfaces for device interactions
• Wearables
• Appliances
• Auto ….
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Alexa VS Lex
• Alexa is coming with the eco-system (devices, install
base, 1st party and 3rd party skills)
• Lex is for single tenant chatbot to integrate with own
users
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Demo
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Amazon Machine Learning
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Amazon Machine Learning service
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Telco churn dataset
• US telco customers, their cell phone plans and usage
• 21 attributes, 3333 rows:
• Customer: State, Area_Code, Phone
• Plan: Intl_Plan, VMail_Plan
• Behavior: VMail_Messages, Day_Mins, Day_Calls,
Day_Charge, Eve_Mins, Eve_Calls, Eve_Charge,
Night_Mins, Night_Calls, Night_Charge, Intl_Mins,
Intl_Calls, Intl_Charge
• Other: Account_Length, CustServ_Calls, Churn
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Telco churn dataset
• US telco customers, their cell phone plans and usage
• 21 attributes, 3333 rows:
• Customer: State, Area_Code, Phone
• Plan: Intl_Plan, VMail_Plan
• Behavior: VMail_Messages, Day_Mins, Day_Calls,
Day_Charge, Eve_Mins, Eve_Calls, Eve_Charge,
Night_Mins, Night_Calls, Night_Charge, Intl_Mins,
Intl_Calls, Intl_Charge
• Other: Account_Length, CustServ_Calls, Churn
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Telco churn dataset
KS, 128, 415, 382-4657, 0, 1, 25, 265.100000, 110, 45.070000, 197.400000, 99, 16.780000, 244.700000, 91, 11.010000, 10.000000, 3, 2.700000, 1, 0
OH, 107, 415, 371-7191, 0, 1, 26, 161.600000, 123, 27.470000, 195.500000, 103, 16.620000, 254.400000, 103, 11.450000, 13.700000, 3, 3.700000, 1, 0
NJ, 137, 415, 358-1921, 0, 0, 0, 243.400000, 114, 41.380000, 121.200000, 110, 10.300000, 162.600000, 104, 7.320000, 12.200000, 5, 3.290000, 0, 0
OH, 84, 408, 375-9999, 1, 0, 0, 299.400000, 71, 50.900000, 61.900000, 88, 5.260000, 196.900000, 89, 8.860000, 6.600000, 7, 1.780000, 2, 0
OK, 75, 415, 330-6626, 1, 0, 0, 166.700000, 113, 28.340000, 148.300000, 122, 12.610000, 186.900000, 121, 8.410000, 10.100000, 3, 2.730000, 3, 0
AL, 118, 510, 391-8027, 1, 0, 0, 223.400000, 98, 37.980000, 220.600000, 101, 18.750000, 203.900000, 118, 9.180000, 6.300000, 6, 1.700000, 0, 0
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Demo
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Questions