More Related Content Similar to AI and Machine Learning Services (20) More from Amazon Web Services (20) AI and Machine Learning Services1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Osemeke Isibor,
Solutions Architect.
iosemeke@amazon.com
31st October 2017
Artificial Intelligence and Machine
Learning Services on AWS
2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Agenda
• Introduction
• Amazon AI
• Amazon Machine Learning
• Conclusion
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Artificial Intelligence At Amazon
Thousands Of Employees Across The Company Focused on AI
Discovery &
Search
Fulfilment &
Logistics
Enhance
Existing Products
Define New
Categories Of
Products
Bring Machine
Learning To All
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AI on AWS Today
•Zillow
–Zestimate (using Apache Spark)
•Howard Hughes Corp
–Lead scoring for luxury real estate purchase predictions
•FINRA
–Anomaly detection, sequence matching, regression
analysis, network/tribe analysis
•Netflix
–Recommendation engine
•Pinterest
–Image recognition search
• DataXu
–Leverage automated & unattended ML at
large scale (Amazon EMR + Spark)
• Mapillary
–Computer vision for crowd sourced maps
• Upserve
–Restaurant table mgmt & POS for
forecasting customer traffic
• TuSimple
–Computer Vision for Autonomous Driving
• Clarifai
– Computer Vision APIs
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We Help Customers
Put Intelligence At The Heart Of
Every Application & Business
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AI Services
AI Platform
AI Engines
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
Apache
MXNet
TensorFlow Caffe Theano KerasTorch CNTK
Amazon AI: Democratized Artificial Intelligence
Amazon
Machine Learning
EMR & Spark
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Amazon AI
Intelligent Services Powered By Deep Learning
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Amazon AI: Deep Learning Services
RekognitionLex
Life-like Speech Image AnalysisConversational Engine
Polly
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Amazon Lex
Conversational Engine
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The Advent Of Conversational Interactions
1st Gen: Machine-oriented
interactions
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The Advent Of Conversational Interactions
1st Gen: Machine-oriented
interactions
2nd Gen: Control-oriented
& translated
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The Advent Of Conversational Interactions
1st Gen: Machine-oriented
interactions
2nd Gen: Control-oriented
& translated
3rd Gen:
Intent-oriented
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Lex: Build Natural, Conversational
Interactions in Voice & Text
Voice & Text
“Chatbots”
Powers
Alexa
Voice interactions
on mobile, web
& devices
Text interaction
with Slack & Messenger
Enterprise
Connectors
(with more coming) Salesforce
Microsoft Dynamics
Marketo
Zendesk
Quickbooks
Hubspot
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Lex Use Case: Informational Bots
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Lex Use Case: Application Bots
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Some Lex Customers
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Amazon Polly
Life-like Speech
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Polly: Life-like Speech Service
Converts text
to life-like speech
47 voices 27 languages
and voices
Low latency,
real time
Fully managed
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Let’s Hear from Polly..
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“Today in Seattle, WA, it’s 11°F”
‘"We live for the music" live from the Madison Square Garden.’
1. Automatic, Accurate Text Processing
Polly: A Focus On Voice Quality & Pronunciation
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Polly: A Focus On Voice Quality & Pronunciation
2. Intelligible and Easy to Understand
1. Automatic, Accurate Text Processing
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2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
“Richard’s number is 2122341237“
“Richard’s number is 2122341237“
Telephone Number
Polly: A Focus On Voice Quality & Pronunciation
1. Automatic, Accurate Text Processing
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2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
4. Customized Pronunciation
“My daughter’s name is Kaja.”
“My daughter’s name is Kaja.”
Polly: A Focus On Voice Quality & Pronunciation
1. Automatic, Accurate Text Processing
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Polly Use Case: Content Creation
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Polly Use Case: Customer Contact Centre
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Some Polly Customers
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Amazon Rekognition
Image Analysis
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Rekognition: Search & Understand Visual Content
Real-time &
batch image
analysis
Object & Scene
Detection
Facial Detection Face SearchFacial Analysis
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Rekognition: Object & Scene Detection
Bay
Beach
Coast
Outdoors
Sea
Water
Palm_tree
Plant
Tree
Summer
Landscape
Nature
Hotel
99.18%
99.18%
99.18%
99.18%
99.18%
99.18%
99.21%
99.21%
99.21%
58.3%
51.84%
51.84%
51.24%
Category Confidence
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Rekognition: Facial Detection
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Rekognition: Facial Analysis
Emotion: calm: 73%
Sunglasses: false (value: 0)
Gender: female (value: 0)
Mouth open wide: 0% (value: 0)
Eye closed: open (value: 0)
Glasses: no glass (value: 0)
Mustache: false (value: 0)
Beard: no (value: 0)
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Rekognition: Compare Faces
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Rekognition: Facial Search
Facial
verification
Face
Search
Visual Similarity
Search
(compare two faces) (compare many faces) (find similar faces)
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Rekognition: Celebrity Rekogniton
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Rekognition: Image Moderation
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Rekognition Use Case: Image Moderation
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Rekognition Use Case: Sentiment Analysis
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Some Rekognition Customers
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Amazon Machine Learning
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Powerful machine learning technology
Powerful ML technology Pay-as-you-go pricing
Integrates with AWS services Fully managed service
Quick and easy to start
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How It Works :Training your Model
HISTORICAL DATA
XXXXXXXXXXXXXX +
Amazon ML
=
𝑘=0
𝑛
𝑛
𝑘
𝑥 𝑘
MODEL
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How it Works: Recommendation/ Prediction
NEW DATA
XXXXXXXX + =MODEL
Predictions /
Recommendations
Amazon ML
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And a few more use case examples…
Fraud detection Detecting fraudulent transactions, filtering spam emails,
flagging suspicious reviews, …
Personalization Recommending content, predictive content loading,
improving user experience, …
Targeted marketing Matching customers and offers, choosing marketing
campaigns, cross-selling and up-selling, …
Content classification Categorizing documents, matching hiring managers and
resumes, …
Churn prediction Finding customers who are likely to stop using the
service, upgrade targeting, …
Customer support Predictive routing of customer emails, social media
listening, …
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Some Amazon Machine Learning Customers
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DIY Machine Learning
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Conclusion: Get Started
https://aws.amazon.com/amazon-ai/what-is-ai/
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Osemeke Isibor,
Solutions Architect
iosemeke@amazon.com