Azure Offerings for Big data. In Kee Paek Cloud Data Solution Architect Microsoft Korea October. 2016

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2 Azure Offerings for Big data In Kee Paek Cloud Data Solution Architect Microsoft Korea October. 2016

3 Agenda 1. Integrated Big data Platform - Cortana Intelligent Suite 2. Scalable Machine Learning - R & Spark on HDInsight 3. GPUs in Azure for Compute & Visualization

4 Stay ahead of the curve with Cortana Intelligence Suite Business apps People Custom apps Apps Sensors and devices Cortana Intelligence Automated systems Data Intelligence Action

5 Cortana Intelligence combines the services you already know

6 Transform data into intelligent action Data Sources Information Management Data Factory Big Data Stores Data Lake Store Machine Learning and Analytics Machine Learning Intelligence Cognitive Services People Apps Data Catalog SQL Data Warehouse Data Lake Analytics Bot Framework Web Event Hubs HDInsight (Hadoop and Spark) Cortana Apps Mobile Bots Data Sensors and devices Stream Analytics Dashboards & Visualizations Power BI Automated Systems Data Intelligence Action

7 Information Management Data Sources Information Management Data Factory Apps Data Catalog Event Hubs Sensors and devices Data

8 Compose and orchestrate data services at scale Information Management SQL Data Factory INGEST Data Catalog DATA SOURCES <> SQL Event Hubs SQL { } Create, schedule, orchestrate, and manage data pipelines Visualize data lineage Connect to on-premises and cloud data sources Monitor data pipeline health Automate cloud resource management Move relational data for Hadoop processing Transform with Hive, Pig, or custom code

9 Get more value from your enterprise data assets Information Management Data Factory Data Catalog Event Hubs Spend less time looking for data, and more time getting value from it Register enterprise data sources, discover data assets and unlock their potential, and capture tribal knowledge to make data understandable Bridge the gap between IT and the business, allowing everyone to contribute their insights, tags, and descriptions Intuitive search and filtering to understand the data sources and their purpose Let your data live where you want; connect using tools you choose Integrate into existing tools and processes with open REST APIs

10 Ingest events from websites, apps and devices at cloud scale Information Management Data sources SQL Database Machine Learning Data Factory Data Catalog Apps Azure API Management Storage HDInsight Event Hubs Sensors and devices Data Backend Services Event Hubs Stream Analytics Power BI Log millions of events per second in near real time Connect devices using flexible authorization and throttling Use time-based event buffering Get a managed service with elastic scale Get a managed service with elastic scale Reach a broad set of platforms using native client libraries Pluggable adapters for other cloud services

11 Big Data Stores Data Sources Information Management Big Data Stores Data Factory Data Lake Store Apps Data Catalog SQL Data Warehouse Event Hubs Sensors and devices Data

12 A hyper-scale repository for big data analytics workloads Big Data Stores ADL Analytics Data Lake Store SQL Data Warehouse Devices LOB Applications Social Video ADL Store HDInsight R Web Sensors Spark Relational Clickstream Machine Learning A Hadoop Distributed File System for the cloud No fixed limits on file size No fixed limits on account size Unstructured and structured data in their native format Massive throughput to increase analytic performance High durability, availability, and reliability Azure Active Directory access control

13 Elastic data warehouse as a service with enterprise-class features Big Data Stores Data Lake Store Power BI Hadoop SQL Data Warehouse App Service SQL Database SQL Data Warehouse Intelligent App Machine Learning Petabyte scale with massively parallel processing Independent scaling of compute and storage in seconds Transact-SQL queries across relational and non-relational data Full enterprise-class SQL Server experience Works seamlessly with Power BI, Machine Learning, HDInsight, and Data Factory

14 Machine Learning and Analytics Data Sources Information Management Big Data Stores Machine Learning and Analytics Data Factory Data Lake Store Machine Learning Apps Data Catalog SQL Data Warehouse Data Lake Analytics Event Hubs HDInsight (Hadoop and Spark) Sensors and devices Stream Analytics Data Intelligence

15 Easily build, deploy, and share predictive analytics solutions Machine Learning and Analytics Machine Learning Data Lake Analytics HDInsight (Hadoop and Spark) Stream Analytics Simple, scalable, cutting edge. A fully managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions. Deploy in minutes. Azure Machine Learning means business. You can deploy your model into production as a web service that can be called from any device, anywhere and that can use any data source. Publish, share, monetize. Share your solution with the world in the Gallery or on the Azure Marketplace.

16 Big data analytics made easy Machine Learning and Analytics Machine Learning Data Lake Analytics Data Lake Analytics HDInsight (Hadoop and Spark) Stream Analytics SQL DW SQL DB Data Lake Store Storage Blobs SQL DB in a VM Analyze data of any kind and size Develop faster, debug and optimize smarter Interactively explore patterns in your data No learning curve use U-SQL, Spark, Hive, HBase and Storm Managed and supported with an enterprise-grade SLA Dynamically scales to match your business priorities Enterprise-grade security with Azure Active Directory Built on YARN, designed for the cloud

17 Comprehensive set of managed Apache big data projects Machine Learning and Analytics Machine Learning Batch Script SQL NoSQL Streaming In-Memory Data Lake Analytics Map Reduce Pig Hive HBase Storm Spark HDInsight (Hadoop and Spark) Core Engine Stream Analytics Scale to petabytes on demand Process unstructured and semi-structured data Develop in Java,.NET, and more Skip buying and maintaining hardware Deploy in Windows or Linux Spin up an Apache Hadoop cluster in minutes Visualize your Hadoop data in Excel Easily integrate on-premises Hadoop clusters

18 Real-time stream processing in the cloud Machine Learning and Analytics SQL Database Machine Learning Event Hubs Blob Storage Data Lake Analytics HDInsight (Hadoop and Spark) Blob Storage Stream Analytics Event Hubs Table Storage Power BI Stream Analytics Perform real-time analytics for your Internet of Things solutions Stream millions of events per second Get mission-critical reliability and performance with predictable results Create real-time dashboards and alerts over data from devices and applications Correlate across multiple streams of data Use familiar SQL-based language for rapid development

19 Intelligence Data Sources Information Management Big Data Stores Machine Learning and Analytics Intelligence Data Factory Data Lake Store Machine Learning Cognitive Services Apps Data Catalog SQL Data Warehouse Data Lake Analytics Bot Framework Cortana Event Hubs HDInsight (Hadoop and Spark) Sensors and devices Stream Analytics Data

20 Agenda 1. Integrated Big data Platform - Cortana Intelligent Suite 2. Scalable Machine Learning - R & Spark on HDInsight 3. GPUs in Azure for Compute & Visualization

21 Cortana Intelligent Suite Data Sources Information Management Data Factory Big Data Stores Data Lake Store Machine Learning and Analytics Machine Learning Intelligence Cognitive Services People Apps Data Catalog SQL Data Warehouse Data Lake Analytics Bot Framework Web Event Hubs HDInsight (Hadoop and Spark) Cortana Apps Mobile Bots Data Sensors and devices Stream Analytics Dashboards & Visualizations Power BI Automated Systems Data Intelligence Action

22 Infinite world of scalable machine learning logistic regression, linear models, basic statistics, hypothesis testing, k-means, decision trees page rank, collaborative filtering, graph processing, SVD, PCA, Bayesian models, deep learning over various types of networks

23 Use cases of scalable machine learning Retail Financial services Healthcare Manufacturing loyalty programs customer acquisition pricing strategy supply chain mgnt customer churn fraud detection risk & compliance cross-sell & upsell personalization bill collection operational efficiency patient demographics pay for performance demand forecasting pricing strategy supply chain optimization predictive maintenance remote monitoring product recommendations intelligent search routing robotics image, video recognition sentiment analysis text comprehension natural language processing ad placement predictive maintenance robotics bots augmented reality predictive maintenance

24 Scalable machine learning offerings in HDInsight Server

25

26 What is R Language Platform The most popular statistical programming language A data visualization tool Open source What is Community 2.5+M users Taught in most universities New and recent grad s use it Thriving user groups worldwide Ecosystem contributed packages Rich application & platform integration

27 R from Microsoft brings Peace of mind Speed and scalability Flexibility

28 Open Source R "

29 Microsoft R Server /data/binary.csv

30 R Server Parallelized by Spark /data/binary.csv

31 R Server on HDInsight R R R R R R R R R R R Server

32 R Server and Spark resource sharing R Tools for VS R Studio Edge node R server DeployR YARN Default Queue IntelliJ IDEA Jupyter notebooks BI Tools Head node Livy server Thrift server Thrift Queue

33 Parallelized and Distributed Algorithms ETL Data import Delimited, Fixed, SAS, SPSS, OBDC Variable creation & transformation Recode variables Factor variables Missing value handling Sort, Merge, Split Aggregate by category (means, sums) Descriptive Statistics Min / Max, Mean, Median (approx.) Quantiles (approx.) Standard Deviation Variance Correlation Covariance Sum of Squares (cross product matrix for set variables) Pairwise Cross tabs Risk Ratio & Odds Ratio Cross-Tabulation of Data (standard tables & long form) Marginal Summaries of Cross Tabulations Statistical Tests Chi Square Test Kendall Rank Correlation Fisher s Exact Test Student s t-test Predictive Statistics Sum of Squares (cross product matrix for set variables) Multiple Linear Regression Generalized Linear Models (GLM) exponential family distributions: binomial, Gaussian, inverse Gaussian, Poisson, Tweedie. Standard link functions: cauchit, identity, log, logit, probit. User defined distributions & link functions. Covariance & Correlation Matrices Logistic Regression Predictions/scoring for models Residuals for all models Variable Selection Stepwise Regression Machine Learning Decision Trees Decision Forests Gradient Boosted Decision Trees Naïve Bayes Clustering K-Means Simulation Simulation (e.g. Monte Carlo) Parallel Random Number Generation Custom Parallelization rxdatastep rxexec PEMA-R API

34 Microsoft R Server: scale-out R, Enterprise Class!

35

36 Apache Spark

37 Spark as a Platform Data Sources

38 Spark MLlib algorithms

39 Spark MLlib algorithms in R language

40 Spark MLlib algorithms in Python language

41 60% KNuggets poll (2014) 50% 40% 30% 20% R and Python are two dominant languages 10% 0% R SAS Python SQL Java the poll

42 R and Spark are better together

43

44 Brief history of Deep learning

45 Deep Learning is about big models and big data

46 Deep Neural Network training

47 Scalable Machine Learning offerings in HDInsight R language Python language Scala/Java Server Server + R Ecosystem + Python Ecosystem + Spark Ecosystem + Spark Ecosystem

48 Agenda 1. Integrated Big data Platform - Cortana Intelligent Suite 2. Scalable Machine Learning - R & Spark on HDInsight 3. GPUs in Azure for Compute & Visualization

49 GPU Virtualization Vision Deliver accelerated graphics & compute capabilities in Azure infrastructure High end performance Not Swiss-army knife offering Helps achieve true HPC in the Cloud Close partnership with NVIDIA

50 Media Stream high fidelity video games Encoding and transcoding Image processing Social media sentiment analysis

51 Rendering Visual Effects (VFX) Ray-Tracing rendering Advertising & Marketing CAD Applications in Architecture Simulations

52 GPU Virtualization Technology DDA (Discrete Device Assignment) Entire device is mapped into the VM just as it would be running on bare metal Allows for full access to capabilities of that device as well as allowing the device s native driver to be used Introduced in Windows Server 2016 as part of Hyper-V Pass-through PCIe devices directly to a Guest VM Allows for close to bare-metal performance

53

54 Compute Virtual Machines

55 Tesla K80 It s fast 15x K80 CPU 10x 5x 0x Benchmarks Molecular Dynamics Quantum Chemistry Physics

56 Accuracy Rate of Improvement 100% 100% 100% 95% 90% 85% 80% GPU 84% 88% 93% 96% 95% 90% 85% 80% 90% 80% 70% 60% Top Score 55% 72% 66% 62% 79% 75% 87.5% 83% 86% 75% 70% 72% 74% 75% 70% 50% 40% 45% 39% 65% % %

57

58 Visualization Virtual Machines

59

60 Collaboration with CNTK First class citizen Scalability multi-gpu-multi-vm Performance Internal use-cases across various Microsoft properties and products (DSVM) Data Science VM by Azure Machine Learning N-Series and CNTK works really well together

61 Samples per Second CNTK Performance on DDA Avg. Samples/Sec Linear (Avg. Samples/Sec) CPU 1 GPU 2 GPUs 3 GPUs 4 GPUs Resource

62 NV = Tesla M60 and supports OpenGL & DirectX NC = Tesla K80 and supports CUDA & OpenCL Sign Azure Batch aka.as/tryazurehpc Teradici NVIDIA

63 2016 Microsoft Corporation. All rights reserved.

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