https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/258491250/

2. Head.

3. Reasons to Reuse Not Reinvent
Retain Functionality Increase EfficiencyRetain Investment

4. Amazon Web Services
APPLICATION SERVICE
PLATFORMS
FRAMEWORKS / INTERFACES

5. 01How to create a speech
enabled system for
facial recognition?
Real Use Case

6. 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

7. 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

8. 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?

9. 02
How to create a chat
app with Sentiment Analysis?
Real Use Case

10. 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

11. 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

12. How do you make your app conversational?
Amazon Lex
A service for build conversional interfaces
into your applications using voice and text

13. 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

14. Chat.
Amazon Lex
CHAT BOT
POSITIVE
NEGATIVE
NEUTRAL

15. 03
How to perform
discovering and indexing
of podcast episodes?

16. 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

17. 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

18. Architecture.

19. 04How to build a
recommendation system?
Real Use Case

20. 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

21. 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.

22. Amazon SageMaker (2)

23. Machine Learning Life Cycle
Business
Problem
Re-training
Predictions
No Yes
DataAugmentation
Feature
Augmentation Are
Business
Goals
met?

24. 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

25. Condé Nast, Hybrid. Training
Documents
Similarity Matrix
!", "$ = &. (
"$, !" = &. (
!", !" = !

26. Condé Nast, Hybrid. Recommend
!"#"$%&"'( 2, 55 = 70% → 1234##256 %&"'55
7$"389 :92& = %&"'0, %&"'1, %&"'2 <%'&"= =
>='&%3' <%'&"=, 7$"389 =

27. 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%

28. Amazon SageMaker (&3)
Easy Model Deployment to Amazon SageMaker Hosting

29. Tail.
carriere+meetup@xpeppers.com

30. https://www.meetup.com/it-IT/Amazon-Web-
Services-Rome/