Le potentiel du Machine Learning et de l’analyse prédictive à portée de votre entreprise
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Microsoft Research conducts research across multiple domains to push the boundaries of computing. It has over 1,100 researchers working in locations around the world. Microsoft Research scientists have won over 320 major awards for their work. The document also provides details on Microsoft Research's joint research institutes and areas of research focus, including machine learning and intelligence.
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Le potentiel du Machine Learning et de l’analyse prédictive à portée de votre entreprise
- 2. 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 »
- 3. 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 confondue 1er mondial dans l’industrie du logiciel
- 6. 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
- 7. Joint research institutes INRIA, France Software security; Formal methods; Applications of computer science research to science www.msr-inria.inria.fr University of Trento, Italy Computational tools for systems biology www.cosbi.eu Barcelona Super Computing Centre, Spain Multi core systems; Architectures and programming; Language runtimes www.bscmsrc.eu
- 8. Algorithms and Theory Exploring the theoretical foundations of computing, and efficient algorithms for a wide variety of problems. Communication and Collaboration Enabling people to reach each other easily regardless of network or device. Computational Linguistics Focusing on machine translation, multilingual systems and natural-language processing. Computational Science Providing computational support to unravel the mysteries of the universe. Computer Systems and Networking Improving efficiency in the deployment, operation management and security of distributed applications. Computer Vision Teaching computers to see and understand the visual world. Data Mining and Management Creating systems for accessing and managing large collections of data, and algorithms for finding patterns and insights within the data. Economics and Computation Exploring the connections between economics and computer science, and creating economic models of online systems. Education Applying computing to help people learn. Expanding programs in computer-science education. Gaming Exploring new technologies to enhance the gaming experience, and identifying and developing innovative technologies and curricula to aid in educational activities. Graphics and Multimedia Addressing challenges in displaying complex computer graphics models, in multiresolution signal representations and enhancement, and in compression of geometry and multimedia data. Hardware and Devices Building the hardware that will support the next generation of software. Health and Well-Being Leading innovation in assisted cognition, bioinformatics, synthetic biology, and biomedicine. Human-Computer Interaction Advancing the way users interact with computing devices. Machine Learning and Intelligence Building software that automatically learns from data to create more advanced, intelligent computer systems. Mobile Computing Exploring how to build mobile devices and services that are efficient, responsive, and usable. Quantum Computing Exploiting quantum physics to create a new generation of computing devices. Search, Information Retrieval and Knowledge Management Exploring indexing and classification technologies, entity extraction, and user- experience concepts that help people organize and find information. Security and Privacy Ensuring the privacy and integrity of our computations and data. Social Media Exploring how digital media are changing the way people work, play, and connect with each other. Social Science Exploring how people use computing in their daily lives. Software Development, Programming Principles, Tools, and Languages Improving quality and efficiency throughout the software-development process. Speech Recognition, Synthesis, and Dialog Systems Teaching computers how to both speak and listen. Technology for Emerging Markets Understanding how technologies can address the needs and aspirations of people in the world’s developing communities. Machine Learning and Intelligence Building software that automatically learns from data to create more advanced, intelligent computer systems.
- 9. intelligence will become ambient intelligence from machine learning
- 10. 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 capacité d’apprendre sans avoir besoin d’être explicitement programmés
- 11. 20 ans de Machine Learning chez Microsoft 1992 début de la reconnaissance vocale 2000 système de recommandation dans Commerce Server 2005 Data Mining dans SQL Server 2005 2008 Kinect pour XBOX 2009 Flash Fill pour Excel 2013 2014 Microsoft Azure Machine Learning
- 13. from Machine Learning to Predictive Analysis In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities Crime Fighting Fraud Detection Marketing Advertising Family and Personal Life Human Resources Financial Risk Insurance Healthcare Fault Detection for Safety and Efficiency
- 14. Questions connexes à prendre compte
- 16. Prédire les prochains souscripteurs de crédit automobile Modèle comporte- mental Caractéristiques Succession d’événements Contexte 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…
- 17. ThyssenKrupp Elevator ThyssenKrupp Elevator wanted to gain a competitive edge by focusing on what matters most to its customers in buildings the world over: reliability
- 19. Pier 1 Imports Pier 1 Imports discuss how they predict which product the customer might want to purchase next, helping to build a better relationship with their customers.
- 21. London Underground Bringing the Internet of Things to the London Underground
- 23. Mission Critical Systems Business Analytics Predictive Analysis
- 24. Ambiant Intelligence for a better Customer Experience “Consistent, Personalized, and Self-learning” Customer Business Operations Orders / CRM Inventory / IOT Finance Services External sources Rating Social / Weather Demographics Partners Integrated Enterprise Data Single View of the Customer Information as a service Scores Segmentation High-Value Services Sales Campaign Churn Prices Interaction Management Channels Web Stores Support Devices Lounges Partners Learning
- 25. Ambiant Intelligence for a better Customer Experience “Consistent, Personalized, and Self-learning” Customer Business Operations Orders / CRM Inventory / IOT Finance Services External sources Rating Social / Weather Demographics Partners Integrated Enterprise Data Single View of the Customer Information as a service Scores Segmentation High-Value Services Sales Campaign Churn Prices Interaction Management Channels Web Stores Support Devices Lounge Partners Learning Advanced and Innovative Dashboards from any device
- 26. Crunching des données internes / externes2 Mode opératoire standard pour un projet ML Compréhension du métier et des données de nos clients1 Vérification itérative avec les métiers4Mise en production du modèle prédictif final5 Mise au point des modèles mathématiques3
- 27. Apprentissage supervisé 70%30% « La fiabilité du modèle est de 93% » Test Apprentissage
- 30. BIG DATA / MACHINE LEARNING : un état d’esprit Typologie simplifiée des projets Big Data / Machine Learning Expérimentation Big Data (Data Lab) Industrialisation de la production d’indicateurs Focalisé sur la production rapide de résultats Focalisé 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 » ≠
- 31. Business Value Workshop La 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 journée Problématique Accompagnement sur la mise en place des scénarios identifiés 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 problématique Audience attendue : Pour plus d’informations : BVW@MICROSOFT.COM
- 33. 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.
- 34. 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 ML Studio 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
- 35. Drag & Drop + Best in Class Algorithms
- 36. API examples Green Score, Wealth Score, Giving Score Frequently Bought Together API Recommendations API Anomaly Detection API Lexicon Based Sentiment Analysis Forecasting-Exponential Smoothing Forecasting - ETS+STL Forecasting-AutoRegressive Integrated Moving Average (ARIMA) Binary Classifier API Cluster Model API Survival Analysis API Multivariate Linear Regression API Survival Analysis API Multivariate Linear Regression API Normal Distribution Quantile Calculator Binomial Distribution Quantile Calculator datamarket.azure.com
Notes de l'éditeur
- Family and Personal Life Nokia : prédire avec un jour d’avance la position géographique des consommateurs à partir de l’historique des déplacements et des interactions sur les réseaux sociaux Marketing Advertising FedEx : arrive à prédire quels clients vont se tourner vers la concurrence avec une efficacité entre 65 et 90% ! Amazon : 35% des revenus proviennent du système de recommandations Financial Risk Insurance Allstate : multiplication par trois de la pertinence de la prédiction d’accidents corporels calculée uniquement à partir des caractéristiques des véhicules Citygroup : a créé des modèles de risques pour les prêts bancaires personnalisés au niveau d’une région et d’une industrie (à partir de 30 ans d’historique) Healthcare RiskPrediction : prédit les risques de décès lors d’une opération chirurgicale (mineure, importante ou complexe) Travaux d’universités : ont mis au point un modèle capable de prédire 80% des naissances prématurées à partir d’un simple examen de sang dès la 24ème semaine Human Resources US Air Force : prédit les capacités des candidats à être capable d’occuper des positions LinkedIn : est capable d’enrichir votre profil avec des capacités déduites du contenu écrit Fault Detection for Safety and Efficiency Con Edison : à NYC, prédiction des taux de panne des câbles de distribution d’électricity avec une mise à jour, trois fois par heure, du niveau de risque sur les panneaux de contrôle des opérateurs High-Tech : prédisent la probabilité d’échec d’imprimantes, disques durs… afin de pouvoir charger les camions de réparation en avance de phase Crime Fighting Fraud Detection Los Angeles : des villes comme LA arrivent à prédire les zones dans lesquelles un crime va avoir lieu et peuvent ainsi diriger en avance de phase les patrouilles de police Chicago : les caractéristiques du crime et des victimes aident la police à prédire si le crime va pouvoir être résolu
- I’m sure you’ve heard Satya talk about the Microsoft focus on enabling a cloud-first, mobile-first world. This shouldn’t be translated as abandoning the on-premises world, which is certainly not going anywhere, but instead putting the right tools in the cloud to enable modern business in ways really impossible before. Machine Learning is a great example of this. I spoke earlier about the vast amounts of data that companies are generating and attempting to consume and make sense out of today. Not only do companies have to think about ingesting that data and buying and managing all the systems to do so – they then have to think about buying and managing additional hardware and software for advanced analytics. That’s a show stopper for many companies today. Azure Machine Learning is a fully managed service in the cloud. That means your company literally needs an Azure subscription to utilize this – that’s all. And you don’t even need that to give it a spin, off azure.com/ml, we offer a free version that only requires a Microsoft account ID (LiveID) to get started right away. You can see now how we’re making the traditionally huge expense of this technology a part of the past. Another issue solved with the use of the cloud is the problem around integration. Azure data sources like HDInsight, Azure SQL Database, SQL Server in a Virtual Machine and more can be brought into the modeling environment with a simple drop down. And data that’s not in the cloud can be modeled against by simply dropping a training set from on-premises into the built in storage space in Azure ML. The only data that must be in the cloud is the training set, but once the model is live as a web service it can literally be run across any data, anywhere. We also understand that in terms of the talent base for machine learning implementation, there is a wide range of skills to serve with our service. That is why we have built a tool that is flexible to address the user brand new to machine learning as well as the seasoned programmer in the open source language R. With our battle tested algorithms from businesses like Bing, Xbox, etc., a nascent advanced analytics professional can drag and drop in data sources and experiment with machine learning without writing a line of code. More experienced implementers can drag and drop in their custom R code as well as use one of the over 350 R packages built into Azure ML. Essentially the use of Azure ML can be as simple or complex as your talent and business require. Lastly – and this is the one thing I want to be sure you take away from the presentation – implementers can deploy a model as a web service in minutes. That is unique in the market to Azure ML. What that means is that models that took weeks or months to put into production and apply to their business problem can now be applied to the business problem right away. Additionally, I mentioned how models are typically so hard to put into production due to disconnected systems and languages that once they’re live they tend to stay live – and get stale and useless? Once the developer has wired up the web service to the company dashboard, or PowerBI or the app or any number of uses you can seamlessly adjust the model – that human input I was talking about – with zero additional development work. Thus, the CMO contacts the data scientist and says they’d like to adjust the output on their PowerBI dashboard to account for new variables as a result of a competitors movements – the implementer can make that change and the dashboard can reflect that in hours – not days.
- So what does that look like from an architectural perspective? Machine learning is a technology in which you work from business problem backwards. Let’s say I have an issue of customer churn. I don’t know why my best customers are leaving and I need to find out. I have things like Twitter/Facebook/Blog entries in HDInsight – our Hadoop implementation in the cloud – and it’s streaming in daily from the web. On premises I have my customer sales data and buying behavior. I can then bring in the training set data from HDInsight and a subset of my on-premises customer data into the built-in storage space. I can then model against that training set in ML Studio – which is the playground for the data scientist or advanced analytic developer. In this space the implementer trains and tests the model until she is satisfied that the model will deliver the answer to the question of customer churn. Not only why the customers are departing, but predictive analytics to tell the company which ones are currently at risk based on past data. That way the sales and marketing departments can target those specific customers with the right activities to solve for why they’re leaving in the first place. The implementer then literally pushes a “Yes” button in the tool to send the finished model into staging, with a flag on the Microsoft Azure portal letting the owner of the all-up portal experience know the model is ready to go. Again – this is a unique and differentiated experience with Azure ML – we are the only ones who offer the ability to push a customized model to production this easily and quickly. Once pushed live, this is now surfaced as a web service which can run over any data, anywhere. If this is running over on-premises data, the data is never persisted in the cloud, so again the only data that must be in the cloud is the original training set, which can be anonymized and removed once the modeling is done for those customers with compliance/security concerns around data in the cloud. This finished web service can now be called from the company dashboard, where the CMO can easily consume the results and advise the teams accordingly. And, as the company needs change, the implementer need only to revisit the model in ML Studio, adjust it and push it to staging again to literally have the model swap out underneath the live web service. But what if the company doesn’t have an implementer in house? In that case, they can go right to the Azure Machine Learning Marketplace, where there are live hosted web services already existing to solve common problems such as this. They can be simply hooked up to apps, services and dashboards for this type of solution. This is also a value-add for companies and implementers looking to monetize their own machine learning solutions. Off azure.com/ml on Machine Learning Center we have detailed instructions on how to leverage this to create, monetize and scale your own ML offerings here.
- Combining World Class Algorithms plus a very simple drag and drop interface.
- https://datamarket.azure.com/browse?query=machine+learning