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Head.
Reasons to Reuse Not Reinvent
Retain Functionality Increase EfficiencyRetain Investment
Amazon Web Services
APPLICATION SERVICE
PLATFORMS
FRAMEWORKS / INTERFACES
01How to create a speech
enabled system for
facial recognition?
Real Use Case
Use Case
The aim is to create a
system for facial
recognition that, once the
person has been
recognised, will also
welcome...
How can your applications see the world?
Amazon Rekognition
Identifies objects, people, text, scenes, faces and
activities...
Amazon Polly
Text-to-speech service based on advanced deep
learning technologies to synthesize speech that
sounds like a h...
02
How to create a chat
app with Sentiment Analysis?
Real Use Case
Use Case
PubNub is a leading
provider of real-time APIs
for building chat, device
control and real-time
mapping apps that ...
How to extract insights from text?
Amazon Comprehend
A fully managed and continuously trained service that
helps you extra...
How do you make your app conversational?
Amazon Lex
A service for build conversional interfaces
into your applications usi...
How do you make your app multilingual?
Amazon Translate
A fully managed and continuously trained neural machine
translatio...
Chat.
Amazon Lex
CHAT BOT
POSITIVE
NEGATIVE
NEUTRAL
03
How to perform
discovering and indexing
of podcast episodes?
Use Case
Build a tool that converts
the audio to text and then
build a searchable index of
podcast feeds to discover
infor...
How do you make your app listen?
Amazon Transcribe
A fully managed and continuously trained automatic speech
recognition (...
Architecture.
04How to build a
recommendation system?
Real Use Case
Use Case
Condé Nast Inc. is an
American mass
media company, it attracts
more than 164 million
consumers across its
brands:...
Amazon SageMaker (1)
It is a fully managed service
that provides the quickest and easiest way for
your data scientists and...
Amazon SageMaker (2)
Machine Learning Life Cycle
Business
Problem
Re-training
Predictions
No Yes
DataAugmentation
Feature
Augmentation Are
Busi...
Condé Nast, Hybrid. Data Processing
• Remove! Most frequent words
• Remove! Punctuation, symbols, numbers
• Keep! adjectiv...
Condé Nast, Hybrid. Training
Documents
Similarity Matrix
!", "$ = &. (
"$, !" = &. (
!", !" = !
Condé Nast, Hybrid. Recommend
!"#"$%&"'( 2, 55 = 70% → 1234##256 %&"'55
7$"389 :92& = %&"'0, %&"'1, %&"'2 <%'&"= =
>='&%3'...
Condé Nast, Hybrid. Evaluation
After training no one can say if it's okay or not okay because there could
be another Datas...
Amazon SageMaker (&3)
Easy Model Deployment to Amazon SageMaker Hosting
Tail.
carriere+meetup@xpeppers.com
https://www.meetup.com/it-IT/Amazon-Web-
Services-Rome/
Machine Learning in the AWS Cloud
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Machine Learning in the AWS Cloud

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Machine Learning in the AWS Cloud

  1. 1. Head.
  2. 2. Reasons to Reuse Not Reinvent Retain Functionality Increase EfficiencyRetain Investment
  3. 3. Amazon Web Services APPLICATION SERVICE PLATFORMS FRAMEWORKS / INTERFACES
  4. 4. 01How to create a speech enabled system for facial recognition? Real Use Case
  5. 5. Use Case The aim is to create a system for facial recognition that, once the person has been recognised, will also welcome him/her. The application must be scalable and serverless. Facial Recognition Speech-enabled Application Video Source
  6. 6. How can your applications see the world? Amazon Rekognition Identifies objects, people, text, scenes, faces and activities, as well as detects any inappropriate content inside an image or video. Object & Scene Detection Facial Search & Analysis Celebrity Recognition
  7. 7. Amazon Polly Text-to-speech service based on advanced deep learning technologies to synthesize speech that sounds like a human voice. How can your applications have a voice?
  8. 8. 02 How to create a chat app with Sentiment Analysis? Real Use Case
  9. 9. Use Case PubNub is a leading provider of real-time APIs for building chat, device control and real-time mapping apps that scale globally. PubNub ChatEngine has integrated Amazon machine learning APIs. Cross-lingual Application Conversational Interfaces Sentimental Analysis
  10. 10. How to extract insights from text? Amazon Comprehend A fully managed and continuously trained service that helps you extract insights from unstructured text Sentiment Key PhrasesEntities Languages Topic Modelling
  11. 11. How do you make your app conversational? Amazon Lex A service for build conversional interfaces into your applications using voice and text
  12. 12. How do you make your app multilingual? Amazon Translate A fully managed and continuously trained neural machine translation service that translates text from one language to another 12 Languages & more to come Translate Text Input Real-time Translation
  13. 13. Chat. Amazon Lex CHAT BOT POSITIVE NEGATIVE NEUTRAL
  14. 14. 03 How to perform discovering and indexing of podcast episodes?
  15. 15. Use Case Build a tool that converts the audio to text and then build a searchable index of podcast feeds to discover information without having to listen to a full episode. Not all episode abstracts are equally helpful! Audio Transcribing Text Comprehend Text Indexing
  16. 16. How do you make your app listen? Amazon Transcribe A fully managed and continuously trained automatic speech recognition (ASR) service that takes in audio and automatically generates accurate transcripts Regular & Telephony Amazon S3 Integration Time Stamps & Confidence Scores Punctuation Detect multiple speakers Custom vocabulary
  17. 17. Architecture.
  18. 18. 04How to build a recommendation system? Real Use Case
  19. 19. Use Case Condé Nast Inc. is an American mass media company, it attracts more than 164 million consumers across its brands: VanityFair, Vogue, GQ, etc. It needs a recommendation system to improve customer experience. Data Transformation Model Training Model Deploy
  20. 20. Amazon SageMaker (1) It is a fully managed service that provides the quickest and easiest way for your data scientists and developers to build, train and deploy Machine Learning models… ..from idea to production.
  21. 21. Amazon SageMaker (2)
  22. 22. Machine Learning Life Cycle Business Problem Re-training Predictions No Yes DataAugmentation Feature Augmentation Are Business Goals met?
  23. 23. Condé Nast, Hybrid. Data Processing • Remove! Most frequent words • Remove! Punctuation, symbols, numbers • Keep! adjectives, verbs, adverbs, pronouns • Lemming! remove inflectional and to return the base or dictionary form of a word: said, say.. • NGram! Words that have different meanings if together "New York" WORDS = 0.579 STOP WORDS = 0.421
  24. 24. Condé Nast, Hybrid. Training Documents Similarity Matrix !", "$ = &. ( "$, !" = &. ( !", !" = !
  25. 25. Condé Nast, Hybrid. Recommend !"#"$%&"'( 2, 55 = 70% → 1234##256 %&"'55 7$"389 :92& = %&"'0, %&"'1, %&"'2 <%'&"= = >='&%3' <%'&"=, 7$"389 =
  26. 26. Condé Nast, Hybrid. Evaluation After training no one can say if it's okay or not okay because there could be another Dataset for which you get different results, but we must first find out! • Test Dataset • Aphorisms • Download sentences with labels • 34 Classes • Love • Friendship • Woman • Man • etc • Complex • Ambiguous 5% 40%
  27. 27. Amazon SageMaker (&3) Easy Model Deployment to Amazon SageMaker Hosting
  28. 28. Tail. carriere+meetup@xpeppers.com
  29. 29. https://www.meetup.com/it-IT/Amazon-Web- Services-Rome/

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