Machine Learning - Intro from Microsoft Partner University

This is the generally available PPT from the MS learning sites

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

2. The Need to Know What Could Be…

3. Describing Machine Learning

4. 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

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

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

7. Machine Learning Flow Integrate

8. 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

9. Machine Learning Challenges ? ? ? Expensive Isolated Data Tool Chaos Complexity Consequences Lost opportunities Expensive operative mistakes Traditional Approach Guessing Rules of thumb Trial and error

10. Introducing Azure Machine Learning

11. 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

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

13. Azure Machine Learning How it Works Azure Portal Azure Ops Team ML Studio Data Scientist Azure Portal & ML API service Azure Ops Team 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 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 HDInsight Azure Storage Desktop Data On- Prem Data Power BI Mobile Apps Web Apps Streaming

14. 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

15. Describing Business Scenarios

16. 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

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

18. Summary

19. 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

20. 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

21. 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

22. © 2016 Microsoft Corporation. All rights reserved. Microsoft, Windows, Microsoft Azure, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION