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MACHINE LEARNING
PASCAL BELAUD - MICROSOFT FRANCEDATA INSIGHT / BIG DATA PRACTICE MANAGER
[email protected] - http://aka.ms/Pascal
"The world is moving so fast these daysthat the man who says it can't be done
is generally interrupted by someonedoing it.“
Elbert Hubbard
Innovation: a definition«Innovation is the
ability to create value while bringing
something new in the field and ensuring that
the appropriation of this novelty is
optimum.»
Arnaud Groff, Dr in« Management de l'Innovation &
de la Créativité »
Microsoft Research (MSR)
Redmond (1991) Cambridge (1997) Beijing (1998)
Silicon Valley (2001) Bangalore (2005) New England
(2008)
More than 1,100 brilliant scientists and engineers push the boundaries of computing in multiple research areas and include contributions to Kinect for Xbox 360, work to develop an HIV vaccine, and advancing education techniques in rural communities.
4ème mondial toute industrie confondue1er mondial dans l’industrie du logiciel
Microsoft Research scientists have won more than 320 major awards, including the Turing Award, MacArthur Foundation Fellowship, MIT Technology Review’s TR35 Award, the Draper Prize, IEEE John von Neumann Medal, IEEE Piore Award, the Kyoto Prize, multiple Oscars and a British knighthood.
Microsoft Research Awards
Joint research institutes
INRIA, France
Software security; Formal methods;Applications of computer science research to sciencewww.msr-inria.inria.fr
University of Trento, Italy
Computational tools for systems biology
www.cosbi.eu
Barcelona Super Computing Centre,
SpainMulti core systems; Architectures and programming; Language runtimeswww.bscmsrc.eu
214D053B33BC4EB115CE3286E8062C01D02EC223
Algorithms and TheoryExploring the theoretical foundations of computing, and efficient algorithms for a wide variety of problems.
Communication and CollaborationEnabling people to reach each other easily regardless of network or device.
Computational LinguisticsFocusing on machine translation, multilingual systems and natural-language processing.Computational
ScienceProviding computational support to unravel the mysteries of the universe.
Computer Systems and NetworkingImproving efficiency in the deployment, operation management and security of distributed applications.
Computer VisionTeaching computers to see and understand the visual world.
Data Mining and ManagementCreating systems for accessing and managing large collections of data, and algorithms for finding patterns and insights within the data.
Economics and ComputationExploring the connections between economics and computer science, and creating economic models of online systems.
EducationApplying computing to help people learn. Expanding programs in computer-science education.
GamingExploring new technologies to enhance the gaming experience, and identifying and developing innovative technologies and curricula to aid in educational activities.
Graphics and MultimediaAddressing challenges in displaying complex computer graphics models, in multiresolution signal representations and enhancement, and in compression of geometry and multimedia data.
Hardware and DevicesBuilding the hardware that will support the next generation of software.
Health and Well-BeingLeading innovation in assisted cognition, bioinformatics, synthetic biology, and biomedicine.
Human-Computer InteractionAdvancing the way users interact with computing devices.
Machine Learning and IntelligenceBuilding software that automatically learns from data to create more advanced, intelligent computer systems.
Mobile ComputingExploring how to build mobile devices and services that are efficient, responsive, and usable.
Quantum ComputingExploiting quantum physics to create a new generation of computing devices.
Search, Information Retrieval andKnowledge ManagementExploring indexing and classification technologies, entity extraction, and user-experience concepts that help people organize and find information.
Security and PrivacyEnsuring the privacy and integrity of our computations and data.
Social MediaExploring how digital media are changing the way people work, play, and connect with each other.
Social ScienceExploring how people use computing in their daily lives.
Software Development, ProgrammingPrinciples, Tools, and LanguagesImproving quality and efficiency throughout the software-development process.
Speech Recognition, Synthesis,and Dialog SystemsTeaching computers how to both speak and listen.
Technology for Emerging MarketsUnderstanding how technologies can address the needs and aspirations of people in the world’s developing communities.
Machine Learning and IntelligenceBuilding software that automatically learns from data to create more advanced, intelligent computer systems.
“I believe over the next decade computing will become even more ubiquitous and intelligence will become ambient...This will be made possible by an ever-growing network of connected devices, incredible computing capacity from the cloud, insights from big data, and intelligence from machine learning.”
Qu’est-ce que le Machine Learning ?
Des méthodes et des systèmes qui …
en fonction des données collectées
de nouvelles données en fonction des données collectées
une action étant donné une fonction d’utilité
une structure cachée des données
les données en des descriptions concises
s’adaptent prédisent optimisent extraient résument
Champ d’études qui donne aux ordinateurs la capacitéd’apprendre sans avoir besoin d’être explicitement
programmés
20 ans de Machine Learning chez Microsoft1992début de la reconnaissance vocale
2000système de recommandation dans Commerce Server
2005Data Mining dans SQL Server 2005
2008Kinect pour XBOX 2009
Flash Fill pour Excel 2013
2014Microsoft Azure Machine Learning
from Machine Learning to Predictive Analysis
In business, predictive models exploit patterns found in historical and
transactional data to identify risks and opportunities
Crime FightingFraud Detection
MarketingAdvertising
Family and Personal
Life
Human Resources
Financial Risk
InsuranceHealthca
re
Fault Detection for Safety and Efficiency
Questions connexes à prendre compte
Prédire les prochains souscripteurs de crédit automobile
Modèle compo
rte-mental
Caractéristiques
Succession d’événeme
ntsContexte
Social
Je suis un cadre dans l’informatique de 42 ans, propriétaire de
ma résidence, avec 2 enfants…
… j’ai réalisé deux dépenses de puériculture
supérieures à 200€ chacune dans les trois
derniers mois…
…mes amis viennent de
souscrire des crédits
automobile…
…dans trois semaines aura lieu
le salon de l’automobile Porte
de Versailles…
ThyssenKrupp ElevatorThyssenKrupp Elevator wanted to gain a competitive edge by focusing on what matters most to its customers in buildings the world over: reliability
Pier 1 ImportsPier 1 Imports discuss how they predict which product the customer might want to purchase next, helping to build a better relationship with their customers.
London UndergroundBringing the Internet of Things to the London Underground
Mission Critical SystemsBusiness Analytics
Predictive Analysis
Ambiant Intelligence for a better Customer Experience“Consistent, Personalized, and Self-learning”
Customer
Business OperationsOrders / CRM
Inventory / IOT
Finance
Services
External sourcesRatingSocial / Weather
Demographics
PartnersIntegrated Enterprise Data
Single View of the Customer
Information as a serviceScores Segmentation
High-Value ServicesSales
Campaign
Churn
Prices
InteractionManagemen
t
ChannelsWeb
Stores
Support
Devices
Lounges
Partners
Learning
Ambiant Intelligence for a better Customer Experience“Consistent, Personalized, and Self-learning”
Customer
Business OperationsOrders / CRM
Inventory / IOT
Finance
Services
External sourcesRatingSocial / Weather
Demographics
PartnersIntegrated Enterprise Data
Single View of the Customer
Information as a serviceScores Segmentation
High-Value ServicesSales
Campaign
Churn
Prices
InteractionManagemen
t
ChannelsWeb
Stores
Support
Devices
Lounge
Partners
Learning
Advanced and Innovative Dashboards from any device
Crunching des données internes /
externes2
Mode opératoire standard pour un projet ML
Compréhension du métier et des données de nos
clients1
Vérification itérativeavec les métiers4Mise en production
du modèle prédictif final
5
Mise au point des modèles
mathématiques3
Apprentissage supervisé
Risque
d’overfitting
70%
30%
« La fiabilité dumodèle est de
93% »
Test
Apprentissage
Apprentissage non supervisé
FrauduleuxSuspects
Légitimes
Apprentissage non supervisé
Légitime
FrauduleuxSuspects
Légitimes
Frauduleux
BIG DATA / MACHINE LEARNING : un état d’espritTypologie simplifiée des projets Big Data / Machine Learning
ExpérimentationBig Data (Data Lab)
Industrialisation de la production
d’indicateurs
Focalisé sur la production rapide de
résultatsFocalisé sur les moyens
Scientifique(Exploratoire)
Ingénieur(Top-Down ou Bottom-
Up)
Disruption,Accepter l’erreur
Continuité,Aversion au risque
Métiers« Shadow IT »
IT« Core IT »
Métiers & IT« Fast IT »
≠
Business Value WorkshopLa Data Science au service de vos métiers
MICROSOFT SERVICES
De la Data aux Insights : quels scénarios innovants pour mieux exploiter les données ?
Introduction autour des nouvelles tendances et enjeux du marché ainsi que de la vision de Microsoft sur la Data Science
Compréhension des enjeux métiers du client et des données manipulées par celui-ci
Recensement des intuitions du client Identification des questions « Machine
Learning » intéressant le client et valorisation de celles-ci
Choix de la question la plus pertinente et proposition de pilote pour y répondre
Agenda – ½ journéeIN Problématique
Accompagnement sur la mise en place des scénarios identifiés
OUT
Objectif de l’atelier :• Présenter les tendances et nouveaux usages autour des
données ainsi que les opportunités offertes par la Data Science avec Microsoft
• Imaginer et formaliser un ou plusieurs scénarios cibles pour répondre à vos problématiques métiers
Vue d’ensemble
Préparation :• Identification d’un sponsor client, puis des participants à
inviter• Rendez-vous de qualification avec le sponsor,
1h pour identifier ses enjeux et définir sa problématiqueAudience attendue :
Client (4-5 pers.)
Comité de
direction
Direction Marketin
g
Direction Financièr
e
Autres Directions métiers
Pour plus d’informations : [email protected]
Microsoft Azure Machine Learning Built for a cloud-first, mobile-first world
Fully managed
Integrated Flexible Deploy in minutes
No software to install, no hardware to manage, all you need is an Azure subscription.
Drag, drop and connect interface. Data sources with just a drop down; run across any data.
Built-in collection of best of breed algorithms with no coding required. Drop in custom R or use popular CRAN packages.
Operationalize models as web services with a single click. Monetize in Machine Learning Marketplace.
Business users access results from anywhere, on any device
Delivering Advanced Analytics
• HDInsight• SQL Server VM• SQL DB• Blobs & Tables
Devices Applications Dashboards
Data Microsoft Azure Machine Learning
Storage space
Integrated development
environment for Machine Learning
MLStudio
Business challenge
Business valueModeling Deployment
• Desktop files• Excel spreadsheet• Other data
files on PC
Cloud
Local
Data to model to web services in minutes
http://studio.azureml.net
Web
Clients
API
Model is now a web svc
Monetize this API
Drag & Drop + Best in Class Algorithms
API examplesGreen Score, Wealth Score, Giving ScoreFrequently Bought Together APIRecommendations APIAnomaly Detection APILexicon Based Sentiment AnalysisForecasting-Exponential SmoothingForecasting - ETS+STL Forecasting-AutoRegressive Integrated Moving Average (ARIMA)Binary Classifier APICluster Model APISurvival Analysis APIMultivariate Linear Regression APISurvival Analysis APIMultivariate Linear Regression APINormal Distribution Quantile CalculatorBinomial Distribution Quantile CalculatorAnd more on datamarket.azure.com
