aka.ms/ uber-selfies

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6 Security & Management Platform Services Hybrid Cloud Security Center Portal Azure Active Directory Azure AD B2C Multi-Factor Authentication Media Services API Management Logic Apps Media Analytics BizTalk Services Service Bus Content Delivery Network Web Apps API Apps Service Fabric Mobile Apps Cloud Services Notification Hubs Functions SQL Database & Stretch Database Analysis Services Tabular SQL Data Warehouse Redis Cache Storage Tables DocumentDB Cognitive Services Bot Framework Cortana Azure Search Azure AD Health Monitoring AD Privileged Identity Management Domain Services Backup Automation Operational Analytics Scheduler Key Vault Store/ Marketplace VM Image Gallery & VM Depot Container Service Batch VM Scale Sets RemoteApp Visual Studio VS Team Services Application Insights Mobile Engagement Xamarin HockeyApp HDInsight IoT Hub Data Catalog Event Hubs Machine Learning Data Lake Analytics Service Data Factory Stream Analytics Data Lake Store Power BI Embedded Import/Export Azure Site Recovery StorSimple Infrastructure Services

7 A Z U R E - B I G D ATA & A D V A N C E D A N A L Y T I C S Ingest Store Prep & train Model & serve Business apps Data Factory Data Lake Store Data Lake Analytics Cosmos DB SQL DB Web & mobile apps Custom apps Blobs HDInsight (Hadoop/Spark) SQL Data Warehouse Operational reports Sensors and devices Event Hubs Machine Learning Analysis Services Analytical dashboards Stream Analytics Kafka on HDInsight D A T A I N T E L L I G E N C E A C T I O N

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

9 Advanced Analytics & AI Portfolio Azure Machine Learning Studio (GUI interface, R, Python, operationalization through webservices, Azure only) Azure Machine Learning Services (code-first, Python (R comming), model management, runs locally, in docker Azure /onprem, use your own IDE, operationalize through webservices) A Z U R E SQL Server Machine Learning Services (on-prem & Azure*, license SQL Server, R/Python inside Stored Procedure, operationalization SQL, interface SQL, models stored in table) B U S I N E S S P O W E R U S E R S DATA S C I E N T I S T S Machine Learning Server ON-P R E M I S E S (on-prem & Azure**, license SQL Server, R/Python, pushdown R/Python to Hadoop/Spark, SQL Server, Operationalization module webservices, interface R/Python, use your own IDE)

10 CLUSTER Virtual Machines CLUSTER HDInsight PLATFORM-AS-A-SERVICE Data Lake Analytics Hadoop, Spark, HBase, & Storm User-installed Hadoop distros on a cluster of user-managed VMs Specific Hadoop distro on a Azure-managed VM cluster Big Data as a Service

11 Advanced Analytics or Internet of Things (IoT) Data type usage by vertical Retail Manufacturing Financial Services Health Government Solutions Real-time offers and personalized services Demand forecasting Manufacturing ops Connected cars Predictive Maint. Customer experience Risk assessment Remote health monitoring Population health management Smart buildings Transit and traffic optimization Sentiment analysis Quality Assurance Clickstream and behavior Machine and sensor Sentiment and web Patient vitals Sentiment and web New types of data Point of sales Server logs Sentiment and web Structured and unstructured Server logs Structured and unstructured Genomic data Server logs Server logs Structured and unstructured

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13 How can you use already existing technology? Get knew level of immediate visibility and predictions. Know the current and highly probabble state Have the right products in the right places. Limiting the possibilities of loss and overstock Match the product requirements from differentiated customers Solve the issues before they happen. Get new insights based on the data you already have Remote monitoring Inventory management Personalized offers Predictive maintenance Cross-sell and upsell Demand forecasting Supply chain optimization Product recommendations Operational efficiency Product-asa-service Risk and compliance management Marketing mix optimization New product introduction Customer service improvement New datadriven services

14 A small retail project proof Demand forecasting groceries sales Goods with fast turnover and time sensitive nature Predict sales in different sales locations with variety of parameters Optimize the ordering process in order to achieve minimal leftover and maximal units sale

15 What is the proof about? Create demand prediction for groceries aiming overstock, ordering and logistics optimization products 140 product groups 10 ML test stores, 37 total stores 800 partner stores predictions based on sales and external data target: minimize overstock optional: maximize sales, maximize margin, minimize forced discounts

16 Training on a year data Training on 3 yrs data

17 Different approaches give different result Bayesian linear regresion Algoritmus Negative Log Likelihood Mean Absolute Error Root Mean Squared Relative Absolute Error Error Relative Squared Error Coefficient of Det ermination Boosted decision tree regresion B ayesion linear regresion 642, , , , , , Decision forest regresion Boosted descision tree regresion 10, , , , , Linear regresion Neural network regresion Poisson regresion Descision forest regresion 559, , , , , , Linear regresion 14, , , , , N eural network regresion 32, , , , , P oisson regresion 27, , , , , * The numbers are real

18 Result >88%

19 We combine data from various sources, But its not just the data Exploring currently invisible dependencies Defining scientifically the landscape of your business Models are able to predict with high accuracy Understand your business as never before Minimize losses and make decisions based on facts and high probability Automate processes

20 The result It is possible to model and predict sales with very high accuracy The technology is ready and able to influence your business now There is extreme potential for further development Machine learning is not only a product but a process. With time it gets better and even more accurate

21 Can I try it? Select your product and location We will demonstrate the viability on your data Deploy in scale WW

22 Open the door for more creative future Use technology and understand the customers attitude, interest, frequency and more Personalize deeply and maximize your business impact on the customers Really understand what customers want and be able to react to it immediately

23 Two ways to get started Cortana Intelligence solution templates Accelerate your journey to actionable insights Partner Finished solutions Reference architecture for common scenarios built on best practice design patterns Automated deployment on your Azure subscription Customizable for your needs Supported by a global partner ecosystem Fully finished SaaS applications that you can test drive Leverage cloud flexibility to scale as you need Built with deep vertical expertise and integration e.g. Plexure Retail Personalization e.g. Personalized Offers

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

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28 Upload Images Upload your own labeled images, or use Custom Vision Service to quickly tag any unlabeled images. Train Use your labeled images to teach Custom Vision Service the concepts you want it to learn. Evaluate Use simple REST API calls to quickly tag images with your new custom computer vision model.

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31 Cognitive Understanding Identify objects, people and actions Hear and recognize language Infer emotions and reactions Develop deeper context & understanding over time Conversation as a platform Natural language conversational UI On any canvas e.g. Skype, Slack, Facebook, etc. Intelligent Bots powered by data & the cloud Accessible through personal digital assistants

32 DFC Support Cases Fi l te r: Case Associ ated Vi ew I ncl u de: Por t al t im esheet s Act ive Payr ols Resolved Voiding a payrols Resolved AM :50 GF90917 DFC times card Resolved PM :48 Al GF90989 l A B C D E F G H I J K L M N O P Q R PM S T U V W X Y Z Intelligent customer service Customer Interact via personalized conversations Predict and minimize churn Customer support agent Account Prim a ry Contact Owne r DFC Administrator All Case Number DFC GF90810 DFC T itle ABC Customer Account Pre fe rre d m ethodof Conta ct Annual Revenue Credit Limit Re la te d *Regarding Records Call Status Created on :18 AM :24 Call Schedule Claims assessment call scheduled 08:23 AM Take action in real time VP of operations

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