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© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Building interactive websites while automating
for efficiency with Amazon AI services
Clifford Duke
Solutions Architect, AWS
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Put machine learning in the
hands of every developer
Our mission at
Our approach for machine learning
Customer-focused
90%+ of our ML roadmap is
defined by customers
Multi-framework
Support for the most
popular frameworks
Pace of innovation
200+ new ML launches and major feature
updates in the
last year
Breadth and depth
A wide range of AI and ML services in-
production
Security and analytics
Deep set of security and
encryption features, with robust analytics
capabilities
Embedded R&D
Customer-centric approach to
advancing the state of the art
The AWS ML Stack
Broadest and most complete set of Machine Learning capabilities
VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS
Ground
Truth
AWS
Marketplace
for ML
Neo Augmented
AIBuilt-in
algorithms
Notebooks Experiments Processing
Model
training &
tuning
Debugger Autopilot
Model
hosting
Model Monitor
Deep Learning
AMIs & Containers
GPUs &
CPUs
Elastic
Inference
Inferentia FPGA
Amazon
Rekognition
Amazon
Polly
Amazon
Transcribe
+Medical
Amazon
Comprehend
+Medical
Amazon
Translate
Amazon
Lex
Amazon
Personalize
Amazon
Forecast
Amazon
Fraud Detector
Amazon
CodeGuru
AI SERVICES
ML SERVICES
ML FRAMEWORKS & INFRASTRUCTURE
Amazon
Textract
Amazon
Kendra
Contact Lens
For Amazon Connect
SageMaker Studio IDE
Amazon SageMaker
DeepGraphLibrary
RL Coach
Fully managed data
processing jobs and
data labeling
workflows
One-click collaborative
notebooks and built-in,
high performance
algorithms and models
One-click
training Debugging and optimization
One-click
deployment and
autoscaling
Amazon SageMaker helps you build, train, and deploy models
Visually track and
compare experiments
Automatically
spot
concept drift
Fully
managed with
auto-scaling
for 75% less
Prepare Build Train & Tune Deploy & Manage
101011010
010101010
000011110
Collect and
prepare
training data
Choose or bring
your own
ML algorithm
Set up and manage
environments
for training
Train, debug, and
tune models
Deploy
model in
production
Manage training runs Monitor
models
Validate
predictions
Scale and manage
the production
environment
Add human
review of
predictions
Web-based IDE for machine learning
Automatically build and train models
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How do I let my applications leverage
machine learning?
AI Services
Pre-trained AI services that require
no ML skills or training
Easily add intelligence to your
existing apps and workflows
Quality and accuracy from
continuously-learning APIs
VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS
Amazon
Rekognition
Amazon
Polly
Amazon
Transcribe
+Medical
Amazon
Comprehend
+Medical
Amazon
Translate
Amazon
Lex
Amazon
Personalize
Amazon
Forecast
Amazon
Fraud Detector
Amazon
CodeGuru
Amazon
Textract
Amazon
Kendra
Contact Lens
For Amazon Connect
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine learning APIs for vision
Amazon Rekognition – Image and Video Analysis
Object & scene detection
Facial analysis
Face search/comparison
Use case?
Moderating user-generated content
Policing user-generated content
Age range – 26–43 years
Wearing glasses – 99.9%
Eyes closed – 94%
Mouth open – 96%
Eyes closed – 94%
Barrack Obama – 100%
Not smiling – 60.3%
Female – 100%
Challenges of non-AI approach
• Manual process for checking images – Labor intensive
• Non-uniformity – Results vary from resource to resource
• Scalability – Difficult to keep up with the rate of image
generation
Example: user-generated content moderation
2. Submit picture
4. DetectFaces
8. SearchFaces
- Blacklist
- Whitelist
- Duplicate check
- Persons of interest
1. Live pic
3. Store live pic
Amazon
Rekognition
Lambda Step functions
5. Recognize Celebrities
Amazon
Rekognition
7. Detect Moderation
Labels
9. Store metadata and
analysis Amazon DynamoDB
Elasticsearch
Blacklist images
Amazon
Rekognition
Amazon
Rekognition
Amazon Textract – OCR++
Amazon Textract – How it works
Use case?
Automate traditional document processing
Example: automated document processing
2. Extract form
data
1. Capture
document image
Amazon
Textract
Application
Backend
3. Send data to
backend 4. User
submitted data
loaded into
database
Amazon
DynamoDB
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine learning APIs for chatbots
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Lex:
A service for building conversational interfaces
into your applications using voice and text
Amazon Lex – Features
Text and speech language understanding: powered by
the same technology as Amazon Alexa
Deployment to chat services
(Web/Mobile Apps, Facebook, Kik, Slack, Twilio SMS)
Designed for builders: efficient and intuitive tools to
build conversations; scales automatically
Versioning and alias support@
Amazon Lex Bots – key concepts
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 Nov 30th
Check out Dec 2nd
“Your hotel is booked for Nov
30th”
Amazon Polly
Confirmation: “Your hotel is
booked for Nov 30th”
“Can I go ahead
with the booking?
a
in
Utterances
I’d like to book a hotel
Can you help me book my hotel?
I want to book a hotel in New York City
I want to make my hotel reservations
Slots
Destination City New York City, Seattle, London …
Slot Type Values
Check in Date Valid dates
Check out Date Valid dates
Slot elicitation
I’d like to book a hotel
What date do you check in?
New York City
Sure, what city do you want to book?
Nov 30th Check in
11/30/2017
City
New York City
Amazon Connect
Self-service, cloud-based contact center service
Real time and
historical analytics
High-quality
voice capability
Call
recording
Skills-based routing
[Automatic Call Distribution (ACD)]
Intelligent call center chatbot
Amazon
Connect
Customer
Amazon Lex Lambda:
Fulfillment
DynamoDB:
Customer Data
SNS:
SMS Messaging
Customer calls
Connect to
reschedule an
appointment
Connect calls
Lex chatbot
Lex chatbot calls
Lambda function
to get customer
preferences and
fulfil Intents
Lambda function
sends text message
confirmation via SNS
Customer receives
appointment
confirmation text
message
Lambda
function writes
updates to
DynamoDB
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine learning APIs for language
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How do you extract insights from
unstructured text?
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Comprehend:
A fully managed and continuously trained service that
helps you extract insights from unstructured text
Amazon Comprehend
Sentiment Entities LanguagesKeyphrases
Topic
modeling
Syntax
Amazon Comprehend – Natural Language Processing
Amazon.com, Inc. is located in Seattle, WA
and was founded July 5, 1994 by Jeff
Bezos. Our customers love buying
everything from books to blenders at
great prices
Named Entities
• Amazon.com: Organization
• Seattle, WA : Location
• July 5th,1994: Date
• Jeff Bezos : Person
Keyphrases
• Our customers
• books
• blenders
• great prices
Sentiment
• Positive
Language
• English
Amazon Comprehend – Syntax API
Our customers love buying everything
from books to blenders at great prices
Token
(word)
Part of
Speech
customers Noun
love Verb
books Noun
great Adjective
prices Noun
Supported parts of speech
ADJ – Adjective
ADP – Adposition
ADV – Adverb
AUX – Auxiliary
CCONJ – Coordinating Conjunction
DET – Determiner
INTJ - Interjection
NOUN - Noun
NUM – Numeral
O – Other
PART – Particle
PRON – Pronoun
PROPN – Proper Noun
PUNCT – Punctuation
SCONJ – Subordinating
Conjunction
SYM – Symbol
VERB – Verb
Syntax detection
$ aws comprehend detect-syntax
--language-code 'en' --text 'I love cloud!’
{
"SyntaxTokens": [
{
"TokenId": 1,
"Text": "I",
"BeginOffset": 0,
"EndOffset": 1,
"PartOfSpeech": {
"Tag": "PRON",
"Score": 0.9999802112579346
}
},
...
Sentiment analysis
$ aws comprehend detect-sentiment
--language-code 'en' --text 'I love cloud!’
{
"Sentiment": "POSITIVE”,
"SentimentScore": {
"Mixed": 0.012617903761565685,
"Positive": 0.9599817991256714,
"Neutral": 0.021758323535323143,
"Negative": 0.005641999188810587
}
}
Popular text analytics use cases
Content Personalization
• Understand related documents based on entities, phrases or even topic similarities for trends
analysis, to drive content personalization and recommendations
Semantic Search
• Index entities for boosting and ranking search results
Intelligent data warehouse
• Query unstructured data in relational databases, processing data within the data lake (Amazon S3)
and then inserting it back into the data warehouse
Social Analytics
• Ingest, process and analyze trends from entities and sentiment from social media posts across
Twitter and Facebook
Support for large data sets and topic modeling
STORM
WORLD SERIES
STOCK MARKET
WASHINGTON
LIBRARY OF
NEWS ARTICLES *
Amazon
Comprehend
Audio Input
Example: End-to-end audio analysis
store
Amazon S3
trigger
AWS
Lambda
call
Amazon
Transcribe
in cascade
Amazon
Comprehend
aggregate
Amazon
Athena
analyze
Amazon
QuickSight
Thank you!
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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AWS_HK_StartupDay_Building Interactive websites while automating for efficiency with Amazon AI Services

  • 1. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Building interactive websites while automating for efficiency with Amazon AI services Clifford Duke Solutions Architect, AWS
  • 2. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Put machine learning in the hands of every developer Our mission at
  • 3. Our approach for machine learning Customer-focused 90%+ of our ML roadmap is defined by customers Multi-framework Support for the most popular frameworks Pace of innovation 200+ new ML launches and major feature updates in the last year Breadth and depth A wide range of AI and ML services in- production Security and analytics Deep set of security and encryption features, with robust analytics capabilities Embedded R&D Customer-centric approach to advancing the state of the art
  • 4. The AWS ML Stack Broadest and most complete set of Machine Learning capabilities VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS Ground Truth AWS Marketplace for ML Neo Augmented AIBuilt-in algorithms Notebooks Experiments Processing Model training & tuning Debugger Autopilot Model hosting Model Monitor Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Inferentia FPGA Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Comprehend +Medical Amazon Translate Amazon Lex Amazon Personalize Amazon Forecast Amazon Fraud Detector Amazon CodeGuru AI SERVICES ML SERVICES ML FRAMEWORKS & INFRASTRUCTURE Amazon Textract Amazon Kendra Contact Lens For Amazon Connect SageMaker Studio IDE Amazon SageMaker DeepGraphLibrary RL Coach
  • 5. Fully managed data processing jobs and data labeling workflows One-click collaborative notebooks and built-in, high performance algorithms and models One-click training Debugging and optimization One-click deployment and autoscaling Amazon SageMaker helps you build, train, and deploy models Visually track and compare experiments Automatically spot concept drift Fully managed with auto-scaling for 75% less Prepare Build Train & Tune Deploy & Manage 101011010 010101010 000011110 Collect and prepare training data Choose or bring your own ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models Validate predictions Scale and manage the production environment Add human review of predictions Web-based IDE for machine learning Automatically build and train models
  • 6. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. How do I let my applications leverage machine learning?
  • 7. AI Services Pre-trained AI services that require no ML skills or training Easily add intelligence to your existing apps and workflows Quality and accuracy from continuously-learning APIs VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Comprehend +Medical Amazon Translate Amazon Lex Amazon Personalize Amazon Forecast Amazon Fraud Detector Amazon CodeGuru Amazon Textract Amazon Kendra Contact Lens For Amazon Connect
  • 8. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine learning APIs for vision
  • 9. Amazon Rekognition – Image and Video Analysis
  • 10. Object & scene detection
  • 14. Policing user-generated content Age range – 26–43 years Wearing glasses – 99.9% Eyes closed – 94% Mouth open – 96% Eyes closed – 94% Barrack Obama – 100% Not smiling – 60.3% Female – 100%
  • 15. Challenges of non-AI approach • Manual process for checking images – Labor intensive • Non-uniformity – Results vary from resource to resource • Scalability – Difficult to keep up with the rate of image generation
  • 16. Example: user-generated content moderation 2. Submit picture 4. DetectFaces 8. SearchFaces - Blacklist - Whitelist - Duplicate check - Persons of interest 1. Live pic 3. Store live pic Amazon Rekognition Lambda Step functions 5. Recognize Celebrities Amazon Rekognition 7. Detect Moderation Labels 9. Store metadata and analysis Amazon DynamoDB Elasticsearch Blacklist images Amazon Rekognition Amazon Rekognition
  • 18. Amazon Textract – How it works
  • 19. Use case? Automate traditional document processing
  • 20. Example: automated document processing 2. Extract form data 1. Capture document image Amazon Textract Application Backend 3. Send data to backend 4. User submitted data loaded into database Amazon DynamoDB
  • 21. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine learning APIs for chatbots
  • 22. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Lex: A service for building conversational interfaces into your applications using voice and text
  • 23. Amazon Lex – Features Text and speech language understanding: powered by the same technology as Amazon Alexa Deployment to chat services (Web/Mobile Apps, Facebook, Kik, Slack, Twilio SMS) Designed for builders: efficient and intuitive tools to build conversations; scales automatically Versioning and alias support@
  • 24. Amazon Lex Bots – key concepts 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
  • 25. “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 Nov 30th Check out Dec 2nd “Your hotel is booked for Nov 30th” Amazon Polly Confirmation: “Your hotel is booked for Nov 30th” “Can I go ahead with the booking? a in
  • 26. Utterances I’d like to book a hotel Can you help me book my hotel? I want to book a hotel in New York City I want to make my hotel reservations
  • 27. Slots Destination City New York City, Seattle, London … Slot Type Values Check in Date Valid dates Check out Date Valid dates
  • 28. Slot elicitation I’d like to book a hotel What date do you check in? New York City Sure, what city do you want to book? Nov 30th Check in 11/30/2017 City New York City
  • 29. Amazon Connect Self-service, cloud-based contact center service Real time and historical analytics High-quality voice capability Call recording Skills-based routing [Automatic Call Distribution (ACD)]
  • 30. Intelligent call center chatbot Amazon Connect Customer Amazon Lex Lambda: Fulfillment DynamoDB: Customer Data SNS: SMS Messaging Customer calls Connect to reschedule an appointment Connect calls Lex chatbot Lex chatbot calls Lambda function to get customer preferences and fulfil Intents Lambda function sends text message confirmation via SNS Customer receives appointment confirmation text message Lambda function writes updates to DynamoDB
  • 31. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine learning APIs for language
  • 32. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. How do you extract insights from unstructured text?
  • 33. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Comprehend: A fully managed and continuously trained service that helps you extract insights from unstructured text
  • 34. Amazon Comprehend Sentiment Entities LanguagesKeyphrases Topic modeling Syntax
  • 35. Amazon Comprehend – Natural Language Processing Amazon.com, Inc. is located in Seattle, WA and was founded July 5, 1994 by Jeff Bezos. Our customers love buying everything from books to blenders at great prices Named Entities • Amazon.com: Organization • Seattle, WA : Location • July 5th,1994: Date • Jeff Bezos : Person Keyphrases • Our customers • books • blenders • great prices Sentiment • Positive Language • English
  • 36. Amazon Comprehend – Syntax API Our customers love buying everything from books to blenders at great prices Token (word) Part of Speech customers Noun love Verb books Noun great Adjective prices Noun
  • 37. Supported parts of speech ADJ – Adjective ADP – Adposition ADV – Adverb AUX – Auxiliary CCONJ – Coordinating Conjunction DET – Determiner INTJ - Interjection NOUN - Noun NUM – Numeral O – Other PART – Particle PRON – Pronoun PROPN – Proper Noun PUNCT – Punctuation SCONJ – Subordinating Conjunction SYM – Symbol VERB – Verb
  • 38. Syntax detection $ aws comprehend detect-syntax --language-code 'en' --text 'I love cloud!’ { "SyntaxTokens": [ { "TokenId": 1, "Text": "I", "BeginOffset": 0, "EndOffset": 1, "PartOfSpeech": { "Tag": "PRON", "Score": 0.9999802112579346 } }, ...
  • 39. Sentiment analysis $ aws comprehend detect-sentiment --language-code 'en' --text 'I love cloud!’ { "Sentiment": "POSITIVE”, "SentimentScore": { "Mixed": 0.012617903761565685, "Positive": 0.9599817991256714, "Neutral": 0.021758323535323143, "Negative": 0.005641999188810587 } }
  • 40. Popular text analytics use cases Content Personalization • Understand related documents based on entities, phrases or even topic similarities for trends analysis, to drive content personalization and recommendations Semantic Search • Index entities for boosting and ranking search results Intelligent data warehouse • Query unstructured data in relational databases, processing data within the data lake (Amazon S3) and then inserting it back into the data warehouse Social Analytics • Ingest, process and analyze trends from entities and sentiment from social media posts across Twitter and Facebook
  • 41. Support for large data sets and topic modeling STORM WORLD SERIES STOCK MARKET WASHINGTON LIBRARY OF NEWS ARTICLES * Amazon Comprehend
  • 42. Audio Input Example: End-to-end audio analysis store Amazon S3 trigger AWS Lambda call Amazon Transcribe in cascade Amazon Comprehend aggregate Amazon Athena analyze Amazon QuickSight
  • 43. Thank you! © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.