Nous assisterons probablement à une rupture générationnelle entre les apps avec de l'intelligence artificielle et celles sans. Ces dernières, comme les applications en mode caractères à l'arrivée des interfaces graphiques, auront du mal à perdurer. Azure met à dispositions 3 approches pour ajouter de l'IA dans une app, avec un niveau de difficulté graduel, de l'outil ne nécessitant aucune compétence particulière à celui dédié aux Data Scientistes.

1. Tour de France
Azure PaaS
Lyon 3 Avril, Issy-les-Moulineaux 5 Avril, Lille 10 Avril,
Bordeaux 12 Avril, Toulouse 24 Avril
Aix-en-Provence 26 Avril, Nantes 3 Mai

2. Intervenant
Alex Danvy
Technical Evangelist
Microsoft
@danvy

3. © Microsoft Corporation
Tour de France Azure PaaS
Démarrer sur Azure
Exécuter une application
Stocker des informations
Sécuriser la solution
Accélérer avec le DevOps
Ajouter de l’intelligence
Bonnes pratiques

4. Microsoft’s aim:
Make AI
available to
everyone
We are pursuing AI to empower every person and every
institution ... so that they can go on to solve the most pressing
problems of our society and our economy.
– Satya Nadella, CEO, Microsoft

5. © Microsoft Corporation
We believe AI technology has the power to
amplify human ingenuity and extend our
capabilities so we can achieve more. When
made accessible to everyone, AI will
transform industries, make us more
productive, and help solve society’s biggest
challenges. This intelligent technology is
already improving our lives today and will
change the world tomorrow in ways
unimaginable to us now.

6. © Microsoft Corporation

7. © Microsoft Corporation
Breakthroughs
Enabling
Intelligence
Cloud
Computing
Big Data
Powerful
Algorithms

11. Agent Applications Services Infrastructure
Microsoft AI Portfolio
Cortana Office 365
Dynamics 365
Bot Framework
Cognitive Services
Cortana Intelligence
Cognitive Toolkit
Azure Machine
Learning
Azure N Series
FPGA
Platform
Approach
For every person and every organization

12. Cortana
133 million users | 12 billion questions
Agent
Knows you | Knows your work + life | Knows the world
1,000+ skills and knowledge

13. Applications
Office 365
Dynamics 365
Skype…

14. Intelligence built in to Office 365
Cortana in Outlook – Your Digital PA Translator / Dictator Outlook
Bots in Teams
Focused inbox in Outlook
Translator Word, PPT, OutlookFace Recognition in Stream

15. Seamless typing with the keyboard
that learns from you.

16. Microsoft Translator : mode d’emploi

17. Microsoft Translator : mode d’emploi
1. Choisir le bouton à
droite
2. Rejoindre la conversation en
tapant le code ci-dessous
3. Rentrer le nom et choisir la
langue

18. Skype Translator

19. Seeing AI

21. Services
Bot Framework
Cognitive Services
Cognitive Toolkit
Cortana Intelligence

22. Language
Speech
Search
Machine
Learning
Knowledge Vision
Spell
check
Speech API
Entity linking
Recommendation
API
Bing
autosuggest
Computer
vision
Emotion
Forecasting
Text to
speech
Thumbnail
generation
Anomaly
detection
Custom
recognition
(CRIS)
Bing
image search
Web language
model
Customer
feedback
analysis
Academic
knowledge
OCR, tagging,
captioning
Sentiment
scoring
Bing
news search
Bing
web search
Text analytics
Cognitive Services APIs

23. Microsoft Services | Digital Advisors
Zoom on cognitive services : build applications that understand people
• Faces, images, emotion recognition and video intelligence
• Spoken language processing, speaker recognition, custom speech recognition
• Natural language processing, sentiment and topics analysis, spelling errors
• Complex tasks processing, knowledge exploration,
intelligent recommendations
• Bing engine capabilities for Web, Autosuggest, Image,
Video and News
“on the shelf”
Intelligence
Cognitive
Services

24. Microsoft Services | Digital Advisors
Zoom on cognitive services : Build applications that understand people
“on the shelf”
Intelligence
Cognitive
Services

25. Machine learning & Intelligence Perceptuelle
Analyse de Texte :Analyse d’image & D’émotions
Moteur de recommandation & plus encore:

26. Bot Framework
Your bots – wherever your users are
talking.
Build and connect intelligent bots to interact
with your users naturally wherever they are,
from text/SMS to Skype, Slack, Messenger,
Office 365 mail and other popular services.

27. Customer Service and Support
Make Customer Service Bots more
human and Live Agents more
productive with Enterprise level AI.
Frictionless human-like conversations
Seamless integration that enables contextual
dialogue
Intelligence built on deep reinforcement
learning
I am having trouble setting up a new projector with
my laptop

28. www.botframework.com

32. Azure
Infrastructure

33. Excel
Third party
BI tools
Cloud data sources
SQL Database
SQL
Data Warehouse
Advanced analytics
As a Service
Power BI
Power BI
Embedded
SQL Server
Other
data sources
R Studio
Visual Studio
+ R for VS addin
Authoring and
development tools
On-premises
data sources
Teradata
Oracle
…
Gateway
Cloud
visualization tools
On-premises
visualization tools
Azure
Machine Learning
Analytics
Platform System
Other data
sources
Micosfot R Server /
R Services for SQL 2016

34. Azure
AI Supercomputer

35. © Microsoft Corporation

36. © Microsoft Corporation

37. The Cortana Intelligence suite covers all of these dimensions
“on the shelf”
Intelligence

39. Microsoft AI
Agent Applications Services Infrastructure
For every person and every organization

40. © Microsoft Corporation
Tools

41. What is Machine Learning? (I)
Age Income Education Gender Housing
61 $65,000 Moderate F Own
42 $72,000 High F Rent
18 $25,000 Moderate M Other
22 $36,000 Low M Rent
31 $52,000 High M ?

42. What is a Machine Learning? (II)

43. Apps + insights
Social
LOB
Graph
IoT
Image
CRM INGEST STORE PREP & TRAIN MODEL & SERVE
Data orchestration
and monitoring
Data lake
and storage
Hadoop/Spark/SQL
and ML
.
IoT
Azure Machine Learning
T H E A I D E V E L O P M E N T L I F E C Y C L E

44. © Microsoft Corporation
Azure Machine Learning
Studio

45. Azure Machine Learning Studio
Platform for emerging data scientists to
graphically build and deploy experiments
• Rapid experiment composition
• > 100 easily configured modules for
data prep, training, evaluation
• Extensibility through R & Python
• Serverless training and deployment
Some numbers:
• 100’s of thousands of deployed models
serving billions of requests

47. © Microsoft Corporation
Visual Studio Tools for AI

48. Visual Studio extension with deep integration to
Azure ML
End to end development environment, from new
project through training
Support for remote training
Job management
On top of all of the goodness of Visual Studio
(Python, Jupyter, Git, etc)
Visual Studio Tools for AI

49. © Microsoft Corporation
Workbench

50. Windows and Mac based
companion for AI development
Full environment set up (Python,
Jupyter, etc)
Embedded notebooks
Run History and Comparison
experience
New data wrangling tools
What Is It?

51. AI Powered Data Wrangling
Rapidly sample, understand, and
prep data
Leverage PROSE and more for
intelligent, data prep by example
Extend/customize transforms and
featurization through Python
Generate Python and Pyspark for
execution at scale

52. Experiment
Manage job for local and cloud experiments
Find support for Spark + Python + R (roadmap)
Execute jobs locally, on remote VMs (scale up),
on Spark clusters (scale out), or SQL on-premises
Create with Git-backed experimentation tracking of code,
config, parameters, and data
Discover and compare with detailed historical metadata

53. © Microsoft Corporation
Azure ML
Experimentation

54. Azure Machine Learning Services - Overview
Spark
SQL Server
Virtual machines
GPUs
Container services
Notebooks
Azure Machine Learning Workbench
Visual Studio Code Tools for AI
Visual Studio Tools for AI
PyCharm
SQL Server
Machine Learning Server
O N - P R E M I S E S
E D G E C O M P U T I N G
Azure IoT Edge
Experimentation and
Model Management
A Z U R E M A C H I N E L E A R N I N G S E R V I C E S T R A I N & D E P LO Y O P T I O N S
A Z U R E

55. Local machine
Scale up to DSVM
Scale out with Spark on HDInsight
Azure Batch AI (Coming Soon)
ML Server
Experiment Everywhere
A ZURE ML
EXPERIMENTATION
Command line tools
IDEs
Notebooks in Workbench
VS Code Tools for AI

56. Manage project dependencies
Manage training jobs locally, scaled-up or
scaled-out
Git based checkpointing and version control
Service side capture of run metrics, output logs
and models
Use your favorite IDE, and any framework
Experimentation service
U S E T H E M O S T P O P U L A R I N N O V A T I O N S
U S E A N Y T O O L
U S E A N Y F R A M E W O R K O R L I B R A R Y

57. DOCKER
Single node deployment
(cloud/on-prem)
Azure Container Service
Azure IoT Edge
Microsoft ML Server
Spark clusters
SQL Server
Deploy Everywhere
A ZURE ML
MODEL MANAGEMENT

58. • Deployment and management of models as HTTP
services
• Container-based hosting of real time and batch
processing
• Management and monitoring through Azure
Application Insights
• First class support for SparkML, Python, Cognitive
Toolkit, TF, R, extensible to support others (Caffe,
MXnet)
• Service authoring in Python

59. AI Powered Spreadsheets

60. © Microsoft Corporation
Data Science VM

61. What is the Data Science VM?
https://aka.ms/dsvm/overview

62. © Microsoft Corporation
Example: Jabil

64. 830022 //Jabil/FAC/5/830022/721PASS.jpg
170643 //Jabil/FAC/0/170643/78PASS.jpg
960841 //Jabil/FAC/8/909078/553failure4.jpg
568208 //Jabil/FAC/7/568208/342PASS.jpg
927029 //Jabil/FAC/0/927029/(370)PASS.jpg
384149 //Jabil/FAC/6/384149/591PASS.jpg
212419 //Jabil/FAC/6/212419/281Pass.jpg
338586 //Jabil/FAC/9/338586/982FAIL6.jpg
224680 //Jabil/FAC/6/224680/710PASS.jpg
259150 //Jabil/FAC/9/259150/830fail(1).jpg
126660 //Jabil/FAC/9/126660/426FAIL4.jpg
410581 //Jabil/FAC/8/410581/155PASS.jpg
64342 //Jabil/FAC/6/64342/739FAIL3.jpg
254849 //Jabil/FAC/3/254849/133Fail4.jpg
115092 //Jabil/FAC/8/115092/341PASS.jpg
391475 //Jabil/FAC/9/391475/410FAIL6.jpg
751809 //Jabil/FAC/7/751809/730PASS.jpg
332909 //Jabil/FAC/6/332909/582FAIL3.jpg
437726 //Jabil/FAC/1/437726/935FAIL(7).jpg
86629 //Jabil/FAC/6/86629/559PASS.jpg
499437 //Jabil/FAC/4/499437/933PASS.jpg
107072 //Jabil/FAC/5/107072/392FAIL6.jpg
709752 //Jabil/FAC/8/709752/471FAIL6.jpg
500667 //Jabil/FAC/6/500667/623FAIL2.jpg
961697 //Jabil/FAC/2/961697/(372)PASS.jpg

69. © Microsoft Corporation
HR Employee Skills
Analysis

70. Identification des métiers du digital nécessaires à la
transformation numérique
Analyser les profils et compétences
des postes des sociétés matures
digitalement :
• Réaliser une étude autour d’un
métier emblématique du digital :
le Data scientist
• Identifier le gap des compétences
et préciser la stratégie de
formation et/ou recrutement
pour accompagner la
transformation digitale du groupe
• Identification des soft skills des
Young Potential et profils
influents
Une mise en œuvre en 4
semaines, méthode itérative :
• Extraction des données des
réseaux sociaux
• Segmentation des profils
selon 4 axes d’analyses :
L’entreprise, les
compétences, la formation
et l’école
• Meilleure connaissance
des métiers emblématiques du
digital
• Identification des
collaborateurs internes à
former
• Identification des profils clés
à recruter
• Identification et
développement des Young
Potentials

71. Clustering des compétences
Cluster A
468 pers.
Strategy
Management
Data analysis
Project Management
Big Data
Data mining
Statistics
Business intelligence
Analytics
Business development
Cluster E
296 pers.
Big data
Python
BI
SQL
Machine Learning
Project Management
Machine Learning
R
Data analysis
Matlab
Strategy
Business Development
Business Strategy
Public speaking
Market analysis
Quasi aucun mot-clé « technique »
Aucun mot-clé du Top 20 des mots-
clés
PROFIL BI
Mix technique et soft skill
Business intelligence et statistical
modelling en avant
ML moins en avant que par ailleurs
Les Wannabe
Data Scientists
Les Data
insighters
Les Citizen
Data Scientists
Cluster B
246 pers.
Les
« Hacking
Developers »
Cluster C
605 pers.
Les
« R&D »
Cluster D
180 pers.
Mix technique et soft skills
Data Analysis
Data Mining
Java
Big Data
R
SQL
C++
Hadoop
Data anlysis
(Project) Management
(Business) Strategy
Marketing
Business Development
Leadership
Très forte dominante technique
plutôt orienté développement et
scripting
Python
Java
Machine Learning
C++
SQL
R
Matlab
Data Mining
Microsoft Office
Javascript
LaTex
Big Data
Hadoop
C
Algorithms
Très forte dominante technique
plutôt orienté autour des
technologies de recherche et
développement
Faible
dispersion
Forte
dispersion
SQL
Machine Learning
Python
Data Mining
R
Java
Big Data
C++
Data analysis
Hadoop
Linux
Algorithms
Matlab
Statistics
LaTex

72. APPROCHE BOTTOM-UP
L’analyse des compétences principales déclarées par
les data scientists permet, outre les profils types,
d’affiner l’interprétation des mots-clés, par exemple à
des fins de recherches de compétences spécifiques / de
recrutement
Les pré-requis permettent de ne conserver que les
candidat(e)s crédibles
Les « must-have » permettent de catégoriser les
profils, i.e. appartenance à un des 5 « profils-
types » : les profils ainsi identifiés sont des profils
solides répondant aux critères énoncés
Les différenciateurs sont les « swingers » : ils
permettent de trouver les skills spécifiques (i.e.
signaux faibles) requis sur ce projet / recrutement
(expérience précédente spécifique, etc.)-
+
-
+Importance
Potentieldedifférenciation
PRE-REQUIS
“MUST-HAVE”
DIFFÉRENCIATEURS
Compétences différenciantes /
challengers
ex : leadership
1
Compétences associées aux 5
segments/profils identifiés
Ex: SQL, C++, Hadoop, Big Data,
Strategy
Python
Java
Machine Learning
R
Data mining
2
3

73. © Copyright Microsoft Corporation. All rights reserved.