Transform Data into Actionable Insights with Azure Data + AI

1. Machine Learning and Deep LearningTransform data into actionable insights Date

2. Azure data + AI Data AI Modernization on premises Hybrid data estate Cloud Native Apps Cloud Scale Analytics Custom AI Pre-Built AI Conversational AI SQL Server 2017 Azure SQL Database Azure DB for MySQL & PostgreSQL Azure Cosmos DB Azure SQL DW Azure Databricks Azure Databricks Azure ML Services Cognitive Services Bot Services Seamlessly integrate with the Azure portfolio Azure active directory ExpressRoute OMS KMS Visual studio tools

3. Advanced analytics is driving innovation across companies Marketing Sales Service Finance Operations Workforce Product recommendation Product recommendation Lead scoring Chatbots Financial forecasting Predictive maintenance Predictive maintenance Employee insights Customer insights Sales insights Virtual assistants Cash flow forecasting Demand forecasting Demand forecasting HR insights Churn analytics Dynamic pricing Waiting line optimization Risk management Quality assurance Resource planning

4. Leading to transformational changes Product recommendation Predictive maintenance Demand forecasting The average size of a singlecart has decreased Unplanned downtime resultsin cost overruns Solar energy productionis inconsistent Provide personalized digitalcontent to shoppers Predict when maintenanceshould be performed Align energy supplywith the optimal markets + Increase cart size Minimize downtime Maximize revenue ASOS delivers 15.4 million personalized experiences with 33 orders per second Hybrid solution predicts onboard water usage, saving $200k/ship/year Distributed power generation increases revenue by over €100 million

5. But not everyone has seen success 85% of organizations have started data-driven initiatives,but only 37%report success1 Why? Technology demandsa power-intensive infrastructure Data is exponentially increasing in volume Security, latency, and degradation concerns from data transfer ML and AI models are difficult to deploy to the edge and applications 1:ESG, 2017

6. What approach should I take to ensure success?

7. Done Microsoft has a recommended reference architecture Ingest Store Prep and train Model and serve Azure HDinsight (Apache kafka) Data science and AI Azure cosmos DB Intelligent apps Streaming data Power BI Azure databricks Data engineering Azure SQL data warehouse Azure analysis services Azure data factory Azure blob storage Batch data

8. Prep and train A B C Collect and prepare data Train and evaluate model Operationalize and manage

9. Done Introducing Azure Databricks Fast, easy, and collaborative Apache Spark™-based analytics platform Increase productivity Built with your needs in mind Role-based access controls Effortless autoscaling Live collaboration Build on a secure, trusted cloud Enterprise-grade SLAs Best-in-class notebooks Simple job scheduling Scale without limits Seamlessly integrated with the Azure Portfolio

10. Collect and prepare data

11. Data preparation is no simple task Key blockers Resulting issues Legacy systems + Exponential data Security Latency Degradation Collect = An overwhelming majority of technical decision makers confirmed their teams spend 40-80%of their time preparingdata1 Collect and prepare Understand Incongruent data + Ineffective tools Critical issues are missed Flawed data is passed downstream = Transform Manpower constraints Misleading insights Inability to replicate and revert Lack of automation + Lack of documentation = 1:TDWI, 2018

12. Done Data collection and prep requirements • Ask for Nishant: • Fill in bullets for ingest • Fill in bullets for understand and transform Ingest TBD TBD TBD Understand and transform TBD TBD Store TBD Capacity TBD Performance TBD Multiple tiers Multiple object sizes Parallel I/O HDFS compatible Encryption

13. Data collection and preparation architecture Ingest Store Understand and transform Azure Data Factory Azure Blob Storage Azure Databricks

14. Data collection and prep hero services Azure Data Factory Azure Blob Storage Azure Databricks Access and ingest data with built-in connectors Get up andrunning quickly Easily process structured and unstructured data from distributed sources Build scalable data flow with codeless UI, or write with your language of choice Economically store any amount and type of data Quickly visualize data and apply transformations in an intuitive notebook environment Schedule, run, and monitor your pipelines with comprehensive control Leverage high bandwidth, high throughput, and low-latency writes Securely collaborate across varied roles and access levels with native Azure Active Directory integration

15. Done Data understanding services Comparing Notebooks in Azure Databricks against other IDEs Notebooks in Azure Databricks Other IDEs Requires software installation No Yes Execution environment Azure Databricks only Pieced together, disparate solutions Serverless service Yes No Kernels supported Spark Python, PySpark Languages supported Python, Scala, R, SQL, Bash Shell Python, SQL, Bash Shell Provides extensive visualizations library in addition to supporting 3rd party libraries. Supports standard Jupyter Notebook visualizations and libraries like Matplotlib Visualizations Full Azure Active Directory integration Supports role-based access control No Collaborative workspaces Simultaneous, multi-user collaboration No Run notebooks as scheduled jobs Yes No Source control GitHub, Bitbucket Yes, but not optimal

16. Train and evaluate

17. Done Modeling requirements • Ask for Nishant: • Validate no additional bullets for modeling Modeling Scale for training Choice of language Choice of algorithms Capture training history Enable collaboration & review Graphical tools Support for deep learning

18. Train and evaluate machine learning architecture For Cloud environments Model MGMT, experimentation, and run history MS ML Services Azure ML Services • 01 Raw data Operational stores Cosmos DB Machine learning Azure Databricks Reference data Data warehouse SQL DW Azure Databricks Scale out clusters

19. Training and evaluating with structured data For on-premises environments Server SQL Server ML Machine learning ML Server Scale out clusters HDFS ML

20. Train and evaluate with unstructured data For on-premises environments Scale out clusters Machine learning ML Server HDFS ML

21. Azure Databricks for machine learning modeling Fast, easy, and collaborative Apache Spark™-based analytics platform Frameworks Tools Infrastructure • Use best-in-class notebooks to quickly access model performance and revert when needed • TensorFlow, Keras, and XGBoost, all installed and configured for Spark clusters • Provision autoscaling clusters on-demand • Schedule notebook activities as jobs in and let Azure Data Factory orchestrate the rest • Leverage parallelized ML algorithms from battle-tested libraries • Enable distributed, multi-GPU training with Horovod via a native runtime • Capture model telemetry at every stage to enable reproduceable results • Get seamless updates of the Spark stack to ensure uninterrupted operations • Take advantage of Azure ML Services to for simple Kubernetes Cluster deployments

22. Custom IDEs Build in your environment of choice R studio PyCharm Notebooks Visual studio tools for AI

23. Operationalize and manage

24. Operationalize and manage requirements Operationalize Scale for scoring Accessible Available everywhere A Manage B TBD C TBD TBD

25. Operationalization and management architecture Scale out clusters Containers ACI AKS Model MGMT, experimentation, and run history Azure ML Services Notebooks Azure Databricks Docker IoT edge Operational stores CosmosDB Intelligent Apps Data warehouse SQL DW Power BI

26. Azure Machine Learning Services Bring AI to everyone with an end-to-end, scalable, trusted platform Boost your data science productivity Built with your needs in mind GPU-enabled virtual machines Low latency predictions at scale Increase your rate of experimentation Integration with popular Python IDEs Role-based access controls Model versioning Deploy and manage yourmodels everywhere Automated model retraining Seamlessly integrated with the Azure Portfolio

27. Deployment environments Azure Container Instances Azure Kubernetes Service Run containers without having to manage servers, virtual machines, or higher-level services Create a Kubernetes cluster in minutes with just a few clicks Containers Expose your containers directly to the internet with an IP address and fully qualified domain Use your favorite Kubernetes tools like Helm for service deployments and Draft for app deployments Quickly and efficiently scale to maximize your resource utilization without having to take your applications offline Secure your applications with guaranteed hypervisor-level isolation Azure Cosmos DB Azure SQL Datawarehouse Seamlessly create your analytics hub with native connectivity to data integration and visualization services Use a database built for low latency and massively scalable applications Applications and BI tools Elastically scale throughput and storage worldwide and only pay for what you need Improve query performance with up to 128 concurrent queries and scale to unlimited queries with Azure Analysis Services integration Get turnkey global distribution and replicate data to any number of regions for fast, responsive access Provision thousands of compute cores in under five minutes and scale to petabyte in hours

28. Model deployment options A side-by-side comparison of capabilities and features • Azure Machine Learning • SQL Server or SQL Database • Azure Databricks • Scoring interface provided • Web service • Notebook or Job • T-SQL stored procedure • SQL Server, Hadoop • Azure Databricks cluster, model export • AKS, ACI • Deployment environments • AKS, ACI edge via AML • SQL Server 2017 database instance on-premises or in Azure VM • IoT, IoT edge • IoT, IoT edge via AML • Spark and Batch AI • Scalability of scoring interface Scales by deploying more instances in Azure Container Services • Can scale across cluster resources • Limited to capacity of single server Load the trained model from storage and apply to scoring in notebook in Python, Scala, R, or SQL. Need to author Python or R code within a T-SQL stored procedure that loads the trained model from a table where it is stored and applies it in scoring. Create a Docker image that contains scoring service, model, and dependencies • Scoring requirements • Model packaging • Serialized to table Serialized to storage Docker image

29. What are companies looking to do next? Deep neural networks will be a standard tool for 80% of data scientists1 20%of companies will dedicate workers to monitor neural networks1 30%of net new revenue growth from industry-specific solutions will include AI1 2018 2019 2020 2021 2022 90%of modern analytics platforms will feature natural-language generation1 More than 40%of data science tasks will be automated1 1 in 5workers engaged in mostly nonroutine tasks will rely on AI to do their jobs2 1 “100 Data and Analytics Predictions Through 2021”, Gartner, 2017. 2 “Predicts 2018: AI and the Future of Work”, Gartner, 2018.

30. Deep learning with Azure

31. Keras TensorFlow Train and evaluate AI and DL models MS cognitive toolkit PyTorch Scale out clusters Azure databricks Azure ML Services Batch AI Notebooks Azure databricks Scale out clusters

32. Azure Databricks for deep learning modeling Fast, easy, and collaborative Apache Spark-based analytics platform Frameworks Tools Infrastructure • Use HorovodEstimator via a native runtime to enable build deep learning models with a few lines of code • Full Python and Scala support for transfer learning on images • Leverage powerful GPU-enabled VMs pre-configured for deep neural network training • Load images natively in Spark DataFrames to automatically decode them for manipulation at scale • Seamlessly use TensorFlow, Microsoft Cognitive Toolkit, Caffe2, Keras, and more • Automatically store metadata in Azure Database with geo-replication for fault tolerance • Simultaneously collaborate within notebooks environments to streamline model development • Use built-in hyperparameter tuning via Spark MLLib to quickly drive model progress • Improve performance 10x-100x over traditional Spark deployments with an optimized environment

33. Additional deep learning tools Deep Learning Virtual Machine Batch AI Automatically scale virtual machine clusters with GPUsor CPUs Designed and pre-configured specifically for GPU-enabled instances Develop your models with long-running batch jobs, iterative experimentation, and interactive training Fully integrated with Azure AI training service to provide capacity for parallelized AI training at scale Support for any deep learning or machine learning framework Get started in seconds with example scripts and sample data sets

34. Deploy AI models to devices on the edge Azure ML Services Qualcomm QCS603 Pre trained Solutions Machine learning Model management Azure Databricks AI Frameworks IoT Edge Vision AI dev. kit IDEs

35. Deploy AI models to devices on the edge flow Message passing TensorFlow Caffe Native Original NW Trained/retrained Scoring file e.g. MobileNet model Service or app Co-located Additional images

36. Machine learning and deep learning, when to use what? Build with Sparkor other engines? Spark ML, SparkR, SparklyR TensorFlow, Keras, MS Cognitive Toolkit, ONNX, Caffe2 Python TensorFlow, Keras, MS Cognitive Toolkit, ONNX, Caffe2 Which experiencedo you want? Code first Jobs Visual tooling Notebooks (cloud) AML (Preview) (cloud) AML Studio (On-prem)ML Server Deployment target Azure Databricks What engine(s) do you want to use? On-prem Hadoop SQL Server SQL Server Hadoop Azure Batch DSVM Spark Spark

37. Microsoft’s comprehensive AI portfolio Pre-built AI Custom AI Notebooks Other IDEs, CLI VS Tools for AI Bot Framework Azure Databricks Cognitive Services Azure ML Services Deep learning frameworks AI on data Cognitive Toolkit TensorFlow ONNX PyTorch Others Train Deploy Spark DSVM Batch AI Cosmos DB SQL DB SQL DW Data Lake ACS, AKS, ACI CPU CPU, GPU, FPGA IoT Edge

38. Get started quickly with AI

39. Additional tools to start today Data Science Virtual Machine Azure Machine Learning Studio Author models in a browser-based, drag-and-drop environment Quickly spin up short-term experimentation and evaluation with near-zero setup Use best-in-class algorithms, hundreds of built-in R and Python packages, and support for custom code Scale vertically and horizontally with on-demand, elastic capacity Deploy your model into production as a web servicein minutes Get started in seconds with examples, templates, and sample notebooks

40. Azure ML Packages Python pip-installable extensions for Azure Machine Learning Quickly build and deploy models for… Computer vision Forecasting Text analytics

41. Easily tap into functionality with Developer AI

42. Developer AI pattern Business process Data AI Channels User input Conversation AI LOB apps Text Dynamics365 Pre-built AI On-premises Speech App services IFTTT Custom AI Image Azure Tools Security Logging Auditing Integration

43. Developer AI reference architecture Direct Custom Vision Service Azure services Bing Image Search API Cognitiveservices LUIS Bing Spell Check API API Management Text Analytics API Azure Web Apps App Direct Line Search Azure Storage Power BI Bot Connector Bot Framework Azure Functions Azure Cosmos DB (Data for search) Azure Cosmos DB (Communication Log)

44. Deploy models using Developer AI services Cognitive Services API Management Azure Web Apps App Direct Line Azure Cosmos DB(Data for search) Search Azure Storage Power BI Bot Connector Bot Framework Azure Functions Azure Cosmos DB (Communication Log) Infuse your applications with intelligence Build and manage bots to interact with users Enable apps to harness web-scale search • Develop in integrated environments • Leverage pre-trained models • Process natural language • Make intelligent recommendations • Empower apps with add-free search • Enable apps to classify images

45. Leverage out-of-the-box AI tools and services Cognitive services Bot services Azure search Use pre-built AI services to solve business problems Speed development with a purpose-built environment for bot creation Get up andrunning quickly Map complexinformation and data Infuse intelligence into your bot using cognitive services Reduce complexity with a fully-managed service Allow your apps toprocess natural language Integrate across multiplechannels to reach more customers Use artificial intelligence to extract insights Create a seamless developer experience across desktop, cloud, or at the edge using Visual Studio AI Tools

46. Enterprise scenarios for AI Business processes Transform critical business processes with AI Conversational agents Transform your engagements with customers and employees Intelligent apps Leverage AI to create the future of business applications Of customer interactions powered by AI bots by 2025 Of enterprises using AI by 2020 Applications to include AI by the end of this year 75% 85% 95%

47. AI-enabled devices Custom AI Enterprise AI Developer AI Drones Smart factory Voice-activated speakers Smart kiosks Smart kitchen Smart cameras Smart bath

48. Microsoft’s comprehensive AI portfolio Pre-built AI Custom AI Notebooks Other IDEs, CLI VS Tools for AI Bot Framework Azure Databricks Cognitive Services Azure ML Services Deep learning frameworks AI on data Cognitive Toolkit TensorFlow ONNX PyTorch Others Train Deploy Spark DSVM Batch AI Cosmos DB SQL DB SQL DW Data Lake ACS, AKS, ACI CPU CPU, GPU, FPGA IoT Edge

49. It’s all on

50. Microsoft Azure Microsoft Azure Productive Hybrid Intelligent Trusted Accelerate time to market Optimize your infrastructure Innovate at scale Develop with confidence