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Machine learning

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Saravanan Subburayal
Saravanan Subburayal

This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.

Datos y análisis
1 de 30
Introduction to Azure Machine Learning Saravanan Subburayal Durga Subburaman
• Who says What? • Challenges • Analytics Evolution • Azure Machine Learning • Process Flow • AzureML in a nutshell • Summary • Resources • Conclusion Agenda
-- Wikipedia -- “Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence” -- Arthur Samuel, 1959 -- “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” -- Tom Mitchell, Carnegie Mellon University -- “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” Who says What ML is?
- Analyze the Past data – Use the Past data - Learn from Past data – to predict the future Definition Simplified
Robo v1.0 Enthiran style Robo v2.0 Computer Programming Machine Learning
Analytics Evolution - Gartner Business Intelligence Machine Learning
Expensive Isolated data Huge data Huge Computing Power Consequences • Lost opportunities • Expensive operative mistakes Traditional approach • Guessing • Rules of thumb • Trial and error Challenges Tool chaos Complexity
Azure ML
• Enables powerful cloud-based predictive analytics • Helps to build, deploy and share advanced analytics solutions • Browser based, Rapid Deployment • Connects seamlessly with other Azure data-related services, including: • Azure HDInsight (Big Data) • Azure SQL Database, and • Virtual Machines Azure Machine Learning
• Browser based, no installation required • Visual Composition with end to end support • Rapid experimentation for Better model creation • Best in class ML algorithms (Bing, Xbox) • Easy to use models : just search and use • Effective collaboration with anyone, anywhere • Extensible support for R • Quickly deployable – as Azure Web Service to ML API service Features and Benefits
Integral Components Azure Portal ML Studio ML API service Azure Ops team Data professionals & Data scientists Software developers
Key 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 modeling 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
ML Process flow
Process flow Define Objective Collect Data Prepare Data Train Models Evaluate Models Publish Manage Integrate
Process flow Define Objective I need to predict customer profitability… …to deliver targeted display advertising on the company eCommerce web site, to: • Present relevant product suggestions • Increase sales and profitability
Process flow 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 Collect Data
Process flow • 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 • 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. Prepare Data
Process flow This stage is iterative, and experimentation is required which involves: • Selecting a machine learning algorithm • Defining inputs and outputs • Optimizing by configuring algorithm parameters Model evaluation is critical to determine: • Accuracy, Reliability, Usefulness Train Models Evaluate Models
Process flow First, add a scoring 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 Publish the experiment to the gallery • Learn from others by discovering experiments • Contribute and showcase your experiments Publish
Process flow 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 Integrate
Hands on https://studio.azureml.net/ Hands on • Get the Data • View it (Descriptive statistics) • Update missing values • Remove unnecessary data • Split data for testing • Train Model • Select algorithm • Score Model • Compare 2 algorithms • Two class Logistic regression • Two class booster • Publish as WebService • Test the Service with a sample data
AzureML in a nutshell
Azure Portal Azure Ops Team ML Studio Data Professional HDInsight Azure Storage Desktop Data Azure Portal & ML API service Azure Ops Team Power BI/DashboardsMobile AppsWeb Apps ML API service Application Developer AzureML in a nutshell
Azure Portal Azure Ops Team ML Studio Data Scientist HDInsight Azure Storage Desktop Data Azure Portal & ML API service Azure Ops Team Power BI/DashboardsMobile AppsWeb Apps ML API service Developer 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 AzurePortal&MLAPIservice and the Azure Ops Team • Create ML Studio workspace • Assign storage account(s) • Monitor ML consumption • See alerts when model is ready • Deploy models to web service ML API service and the Application Developer • Tested models available as a URL that can be called from any endpoint Business users easily access results from anywhere, on any device AzureML in a nutshell
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 developers Summary
Quick and easy extensibility with cloud functions such as Power BI, Hadoop (Azure HDInsight) and cloud storage Summary
Resources http://azure.microsoft.com/en-us/services/machine-learning http://azure.microsoft.com/en-us/documentation/services/machine-learning http://azure.microsoft.com/en-us/documentation/articles/machine-learning-faq http://azure.microsoft.com/en-us/pricing/details/machine-learning/ https://gallery.azureml.net
Saravanan Subburayal, Principal Architect, Jeevan Technologies https://www.linkedin.com/in/saranworld Durga Subburaman, Solution Architect, Mobius Knowledge Services https://in.linkedin.com/in/durga-subburaman-52012025

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Machine learning

  • 1. Introduction to Azure Machine Learning Saravanan Subburayal Durga Subburaman
  • 2. • Who says What? • Challenges • Analytics Evolution • Azure Machine Learning • Process Flow • AzureML in a nutshell • Summary • Resources • Conclusion Agenda
  • 3. -- Wikipedia -- “Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence” -- Arthur Samuel, 1959 -- “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” -- Tom Mitchell, Carnegie Mellon University -- “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” Who says What ML is?
  • 4. - Analyze the Past data – Use the Past data - Learn from Past data – to predict the future Definition Simplified
  • 5. Robo v1.0 Enthiran style Robo v2.0 Computer Programming Machine Learning
  • 6. Analytics Evolution - Gartner Business Intelligence Machine Learning
  • 7. Expensive Isolated data Huge data Huge Computing Power Consequences • Lost opportunities • Expensive operative mistakes Traditional approach • Guessing • Rules of thumb • Trial and error Challenges Tool chaos Complexity
  • 9. • Enables powerful cloud-based predictive analytics • Helps to build, deploy and share advanced analytics solutions • Browser based, Rapid Deployment • Connects seamlessly with other Azure data-related services, including: • Azure HDInsight (Big Data) • Azure SQL Database, and • Virtual Machines Azure Machine Learning
  • 10. • Browser based, no installation required • Visual Composition with end to end support • Rapid experimentation for Better model creation • Best in class ML algorithms (Bing, Xbox) • Easy to use models : just search and use • Effective collaboration with anyone, anywhere • Extensible support for R • Quickly deployable – as Azure Web Service to ML API service Features and Benefits
  • 11. Integral Components Azure Portal ML Studio ML API service Azure Ops team Data professionals & Data scientists Software developers
  • 12. Key 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 modeling 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
  • 15. Process flow Define Objective I need to predict customer profitability… …to deliver targeted display advertising on the company eCommerce web site, to: • Present relevant product suggestions • Increase sales and profitability
  • 16. Process flow 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 Collect Data
  • 17. Process flow • 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 • 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. Prepare Data
  • 18. Process flow This stage is iterative, and experimentation is required which involves: • Selecting a machine learning algorithm • Defining inputs and outputs • Optimizing by configuring algorithm parameters Model evaluation is critical to determine: • Accuracy, Reliability, Usefulness Train Models Evaluate Models
  • 19. Process flow First, add a scoring 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 Publish the experiment to the gallery • Learn from others by discovering experiments • Contribute and showcase your experiments Publish
  • 20. Process flow 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 Integrate
  • 21. Hands on https://studio.azureml.net/ Hands on • Get the Data • View it (Descriptive statistics) • Update missing values • Remove unnecessary data • Split data for testing • Train Model • Select algorithm • Score Model • Compare 2 algorithms • Two class Logistic regression • Two class booster • Publish as WebService • Test the Service with a sample data
  • 22. AzureML in a nutshell
  • 23. Azure Portal Azure Ops Team ML Studio Data Professional HDInsight Azure Storage Desktop Data Azure Portal & ML API service Azure Ops Team Power BI/DashboardsMobile AppsWeb Apps ML API service Application Developer AzureML in a nutshell
  • 24. Azure Portal Azure Ops Team ML Studio Data Scientist HDInsight Azure Storage Desktop Data Azure Portal & ML API service Azure Ops Team Power BI/DashboardsMobile AppsWeb Apps ML API service Developer 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 AzurePortal&MLAPIservice and the Azure Ops Team • Create ML Studio workspace • Assign storage account(s) • Monitor ML consumption • See alerts when model is ready • Deploy models to web service ML API service and the Application Developer • Tested models available as a URL that can be called from any endpoint Business users easily access results from anywhere, on any device AzureML in a nutshell
  • 26. 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 developers Summary
  • 27. Quick and easy extensibility with cloud functions such as Power BI, Hadoop (Azure HDInsight) and cloud storage Summary
  • 29.
  • 30. Saravanan Subburayal, Principal Architect, Jeevan Technologies https://www.linkedin.com/in/saranworld Durga Subburaman, Solution Architect, Mobius Knowledge Services https://in.linkedin.com/in/durga-subburaman-52012025