SQL Saturday #462 Parma 28 November, 2015 Azure Machine Learning. From Design to Integration

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1 SQL Saturday #462 Parma 28 November, 2015 Azure Machine Learning From Design to Integration

2 Presenter Introduction Peter Myers Independent BI Expert Bitwise Solutions BBus, SQL Server MCSE, MCT, Data Platform MVP (since 2007) Experienced in designing, developing and maintaining Microsoft database and application solutions (since 1997) Focuses on education and mentoring Based in Melbourne, Australia

3 Sponsors November 28, 2015 #sqlsatparma #sqlsat462

4 Organizers getlatestversion.it November 28, 2015 #sqlsatparma #sqlsat462

5 Session Outline Describing Machine Learning Introducing Azure Machine Learning Demonstrating the Machine Learning Flow Describing Business Scenarios Summary

6 The Need to Know What Could Be

7

8 Describing Machine Learning

9 Machine Learning Subfield of computer science and statistics that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions -Wikipedia

10 I need to add two numbers together num1, num2

11 I need to predict customer profitability Age, Marital Status, Gender, Yearly Income, Total Children, Education, Occupation, Home Owner, Commute Distance

12 Machine Learning Flow Define Objective Manage Collect Data Integrate Publish Prepare Data Evaluate Models Train Models

13 Machine Learning Roles Data Scientist A highly educated and skilled person who can solve complex data problems by employing deep expertise in scientific disciplines (mathematics, statistics or computer science) Data Professional A skilled person who creates or maintains data systems, data solutions, or implements predictive modelling Roles: Database Administrator, Database Developer, or BI Developer Software Developer A skilled person who designs and develops programming logic, and can apply machine learning to integrate predictive functionality into applications

14 Machine Learning Challenges High Competition Strategic Change Expensive Isolated Data Traditional Approach Guessing Rules of thumb Trial and error New Markets Lots of Buzz Words Tool Chaos Complexity Consequences Lost opportunities Expensive operative mistakes DATA SCIENTIST

15 Introducing Azure Machine Learning

16 Azure Machine Learning Enables powerful cloud-based predictive analytics Professionals can easily build, deploy and share advanced analytics solutions Armed with nothing but a browser, professionals can log on to Azure and develop prediction models from anywhere and deploy new analytic models quickly Retains a practically unlimited number of files on Azure Storage and connects seamlessly with other Azure data-related services, including: Azure HDInsight (Big Data) Azure SQL Database, and Virtual Machines

17 Azure Machine Learning How it Works Azure Portal Azure Ops Team ML Studio Data Professionals & Data Scientists ML API service Software Developers

18 Demonstration [Azure Ops Team] Provisioning an Azure Machine Learning Workspace

19 Demonstrating the Machine Learning Flow

20 Machine Learning Flow Define Objective Manage Collect Data Integrate Publish Prepare Data Evaluate Models Train Models

21 Machine Learning Flow Define Objective It is important to start a machine learning project with a clearly defined objective I need to predict customer profitability to deliver targeted display advertising on the company ecommerce web site, to:

22 Machine Learning Flow Collect Data Collecting sufficient volumes of current, clean, and complete data is critical Garbage in Garbage out Datasets can be sourced from: Internal sources, i.e. operational systems, data warehouse, etc. External sources Different formats, i.e. relational, multidimensional, text, map-reduce Combining datasets can enrich data E.g., integrate internal data to external data like weather, or market intelligence data

23 Demonstration [Data Professional] Collecting Quality Data

24 Machine Learning Flow Prepare Data Often significant amounts of effort are required to prepare data for machine learning Transform to cleanse, reduce or reformat Isolate and flag abnormal data Appropriately substitute missing values Categorize continuous values into ranges Normalize continuous values between 0 and 1 Of course, having the required data to begin with is important When designing systems, give consideration to attributes that may be required as inputs for future modeling, e.g. demographic data: Birth date, gender, etc.

25 Demonstration [Data Professional] Preparing the Data for Machine Learning

26 Machine Learning Flow Train Models Evaluate Models This stage is iterative, and experimentation is required to produce an optimal model Model training involves: Selecting a machine learning algorithm Defining inputs and outputs Optimizing by configuring appropriate algorithm parameters Model evaluation is critical to determine: Accuracy Reliability Usefulness

27 Demonstration [Data Professional] Producing a Machine Learning Experiment

28 Machine Learning Flow Publish First, add a prediction experiment Transformational logic is replaced with a re-usable transformation resource Training logic is replaced with a trained model Web service inputs and outputs are added Module properties can be parameterized Prepare the experiment for publication Publish the experiment as a web service Publish the experiment to the gallery Learn from others by discovering experiments Contribute and showcase your experiments

29 Demonstration [Data Professional] Preparing and Publishing the Web Service

30 Machine Learning Flow Manage Manage web services Add and manage endpoints Configure endpoint throttle and tracing Monitor web services Monitor API transactions Monitor how workspaces are used Monitor compute resources Monitor storage space Monetize web services by publishing them to the Azure Marketplace

31 Demonstration [Azure Ops Team] Managing the Web Service

32 Machine Learning Flow Integrate Develop an application that integrates predictive functionality with web service calls Each web service offers two methods: Request/Response Service (RRS) Low latency, highly scalable web service Batch Execution Service (BES) High volume, asynchronous scoring of many records The service can be called by any programming language, from any device, that: Internet connectivity SSL capabilities to perform HTTPS requests The OData endpoint address and API key The ability to parse JSON (by hand or support libraries)

33 Demonstration [Software Developer] Integrating prediction results into an ASP.NET web application

34 Describing Business Scenarios

35 Message for IT Professionals Machine Learning is one of the most popular fields in the discipline of Computer Science, and it is also perhaps the most feared by developers This fear is probably due to the understanding that Machine Learning is a scientific field requiring deep mathematical expertise But Machine Learning has two disciplines: Machine Learning, and Applied Machine Learning IT Professionals can: Apply Machine Learning by acquiring practical hands-on skills that get Machine Learning algorithms to work, rather than the mathematical underpinnings of Machine Learning Integrate predictive functionality into application experiences

36 Business Scenarios Imagine what you could use Machine Learning for Ad targeting Churn analysis Image detection & classification Equipment monitoring Recommendations Forecasting Spam filtering Fraud detection Anomaly detection

37 Summary

38 Azure Machine Learning One Solution for Machine Learning Web Apps Mobile Apps Power BI/Dashboards ML API service Application Developer Azure Portal & ML API service Azure Ops Team ML Studio Data Professional HDInsight Azure Storage Desktop Data

39 Azure Machine Learning One Solution for Machine Learning Web Apps Mobile Apps Power BI/Dashboards Business users easily access results from anywhere, on any device ML API service ML API service and the Application Developer Developer Tested models available as an url that can be called from any end point Azure Portal & and the Azure Ops Team ML API service Azure Portal & ML API service Create ML Studio workspace Assign storage account(s) Monitor ML consumption See alerts when model is ready Azure Ops Team Deploy models to web service ML Studio and the Data Professional Access and prepare data Create, test and train models Collaborate One click to stage for production via the API service Data Scientist HDInsight Azure Storage Desktop Data

40 Machine Learning Process Analytics Today Quick and Easy Extensibility with Cloud Functions including Power BI, Hadoop (Azure HDInsight) and Azure Storage

41 Summary Machine Learning is a subfield of computer science and statistics that deals with the construction and study of systems that can learn from data Azure Machine Learning key attributes: Fully managed No hardware or software to buy Integrated Drag, drop, connect and configure Best-in-class Algorithms Proven solutions from Xbox and Bing R Built In Use over 400 R packages, or bring your own R or Python code Deploy in Minutes Operationalize with a click Machine Learning is now approachable to Data Professionals

42 Resources Azure Machine Learning web site Azure Machine Learning documentation Azure Machine Learning FAQ Azure Machine Learning pricing Note: The Free tier does not require an Azure subscription or a credit card Azure Machine Learning gallery

43 Resources (Continued) Azure Machine Learning blog Videos: PASS Data Science Virtual Chapter Videos: SSW TV: Cloud-Based Machine Learning for the Developer Microsoft Ignite Conference: Session: Cloud-Based Machine Learning for the Developer (4 Sep, 2015) Presenter: Peter Myers /M370

44 Resources (Continued) Book: Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes Publisher: Apress Authors: Roger Barga, Valentine Fontama, and Wee Hyong Tok Paul the Octopus

45 Feedback

46 Thank You

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