L'obiettivo della presentazione è quello di sensibilizzare le aziende nella necessità di introdurre progetti di Big Data in azienda per riconoscere in modo puntuale il proprio utente. Il digital ci permette di raccogliere l'imporonta digitale degli utenti ma richiede di ottenere la loro fiducia per comprendere i loro interessi, abitudini, network etc. Oggi analizziamo dati in tempo reale ma dobbiamo essere in grado, per competere sull mercato, di prevedere gli interessi ed esigenze degli utenti. Per fare questo ci servono sistemi di Machine Learning in grado di apprendere dai dati ed evolversi automaticamente. Durante la presentazione sono stati illustrati i metodi di apprendimento supervisionato e non supervisionato e come il Servizio Machine Learning di Amazon Web Services sia in grado di semplificare un'attività complessa come la costruzione di un modello di Machine Learning.

1. Stefano Dindo
CEO zero12 s.r.l.
@stefanodindo
Big Data: Come usare le Machine
Learning per migliorare il business

2. Everyone is looking for a treasure
Which one?

6. What does this mean in real life?

7. Digital fingerprint is the key
It allows to track User behaviour

9. U
We would like to know everything about our
User
EnvironmentNetwork
Preference
Influencer

10. so we collect data anywhere to find our Wally
Machines
Workplaces
Social Networks
Web/Mobile Apps
Public Events Any other tool or service

11. Introduction
Big Data

13. Data
Volume
Speed
Variety
MB GB TB PB
Batch
Periodic
Near Realtime
Realtime
Table
Database
Photo
Web
Audio
Video
Social
Mobile
unstructured

14. Memory
Data
Economic
Value
Capacity

15. IF YOU COLLECT MORE DATA
YOU CAN GET MORE VALUE
FOR YOUR BUSINESS

16. GENERATE
COLLECT
ANALYZE
ENRICH
VISUALIZE

17. Typical problem
Data Ingestion

18. Streams
Database Source
- Silos -
Batch feeds
0101
0010110
0101001
01
011011
1010
Data Lake
Information storage
Data heterogeneity
Read/Write performance

19. How to analyze data
Typical problem

20. Batch Real-Time Prediction
Report Alerts Forecast

21. Data Predictions

22. Data Predictions
?

24. Data PredictionsModel

25. Data
Batch
Predictions
Model
Real-Time
Predictions

26. Future
Machine Learning

27. Machine learning is the science of
getting computers to act without
being explicitly programmed
Arthur Samuel
“ “

28. Machine Learning
Supervised
Learning
Unsupervised
Learning
The task of inferring
a model from
labeled training data
The task of inferring
a model to describe
a structure from
unlabeled data

29. Example of unsupervised Learning
Clustering

30. Example of unsupervised Learning
Clustering

31. Supervised Learning
Regression
Binary Classification
Multi-class Classification

32. Validation

33. Training from Labeled Data
- Supervised Learning -
70%
30%
Training
Validation
Data

34. Cross-Validation
- Supervised Learning -
1 2 3
Evaluation data sourcesTraining data sources (complement of evaluation)

35. Multiple Validation
- Supervised Learning -
60%
20%
Training
Validation
Data
20%
Test

36. Data Predictions
What technologies are involved?
Huge amount of data
Disk
MapReduce
Hadoop
Spark
Resilient distributed Datasets (RDDs)
Memory
Fast Processing

37. AWS Machine Learning
1 2 3
Creation Optimization Production

38. Is machine learning really
useful to my business?

41. Mario
Maria
www.example.com
Predictive websites
www.example.com

42. What is Your Mission? Building your Model :-)

43. INNOVATION COMPANY
“Learn quickly and Think Well!”

44. “We are an Innovation Company. We design
and develop cutting edge software to drive
our customers’ digital transformation,
through Agile Methodologies and continuous
delivery”

45. WE HELP OUR CUSTOMERS TO
DESIGN
IDEA
CREATE
PRODUCTS
EXTRACT VALUE
FROM DATA
Get powerful ideas
to market fast
We design and develop
new better software
solutions
We collect and analyze
data to help your
decision

46. Discover Experiment Delivery
MVP
Continuous Design
&
Integration
OUR APPROACH
MVP
MVP
MVP
?
?
? ?
? ?
?
End UsersIdeaCustomer & zero12
collaboration meeting
( CanvUX )
Customers feedback

47. Buone Feste ! ;-)
Stefano Dindo
s.dindo@zero12.it
+39 328 2854891
THANK YOU!