1. 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]

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

3. Stock image

4. Describing Machine Learning

5. 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

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

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

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

9. 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

10. 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

11. Introducing Azure Machine Learning

12. 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

13. 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

14. 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

15. 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

16. Describing Business Scenarios

17. 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

18. 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

19. Summary

20. 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

21. 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

22. Resources 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

23. © 2016 Microsoft Corporation. All rights reserved
© 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