Data Platform and Analytics Foundational Training Microsoft C+E Technology Training Data Platform and Analytics Foundational Training Solution Area Data Analytics Solution Advanced Analytics Technology Machine Learning [Speaker Name]

The Need to Know What Could Be… Clipart image sourced from Microsoft Office 2007

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Describing Machine Learning

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

I need to add two numbers together… f( ) num1, num2

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

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

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

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

Introducing Azure Machine Learning

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 Can connect also to SQL Server on-premises

Azure Machine Learning How it Works Mobile Apps Web Apps Streaming Power BI Business users easily access results from anywhere, on any device ML API service Application Developer Azure Portal & ML API service Azure Ops Team Azure Portal Azure Ops Team ML Studio Data Professional HDInsight Azure Storage Desktop Data On-Prem Data

Azure Machine Learning How it Works Mobile Apps Web Apps Streaming 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 Azure Portal & ML API service 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 an url that can be called from any end point Business users easily access results from anywhere, on any device Power BI ML API service Developer Azure Portal Azure Ops Team Azure Portal & ML API service Azure Ops Team ML Studio Data Scientist HDInsight Azure Storage Desktop Data On-Prem Data

Machine Learning Process One Solution for Machine Learning Faster Towards Solutions Mashup of Powerful Algorithms Global Scaling of Solutions via Cloud API Elastic, Pay-as-you-go Model with Low Operative Costs Quick and Easy Extensibility with Cloud Functions including Power BI, Hadoop (Azure HDInsight) and Azure Storage

Describing Business Scenarios

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

Business Scenarios Ad targeting Churn analysis 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

Summary

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

Resources Azure Machine Learning web site http://azure.microsoft.com/en-us/services/machine-learning Azure Machine Learning documentation http://azure.microsoft.com/en-us/documentation/services/machine-learning Azure Machine Learning FAQ http://azure.microsoft.com/en-us/documentation/articles/machine-learning-faq Azure Machine Learning pricing http://azure.microsoft.com/en-us/pricing/details/machine-learning/ Note: The Free tier does not require an Azure subscription or a credit card Azure Machine Learning gallery https://gallery.azureml.net

Resources Azure Machine Learning blog http://blogs.technet.com/b/machinelearning Videos: PASS Data Science Virtual Chapter https://www.youtube.com/channel/UCqB3xWdwjA9soFV6EOu7qfg Videos: SSW TV: Cloud-Based Machine Learning for the Developer http://tv.ssw.com/5916/cloud-based-machine-learning-for-the-developer-peter-myers Microsoft Ignite Conference: Session: Cloud-Based Machine Learning for the Developer (4 Sep, 2015) Presenter: Peter Myers https://channel9.msdn.com/Events/Ignite/Microsoft-Ignite-New-Zealand-2015/M370

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