This document discusses machine learning and Azure Machine Learning. It defines machine learning and provides quotes about machine learning from experts. It then lists examples of machine learning applications and business uses. The document outlines skills needed for applied machine learning and describes the Azure Machine Learning process and tools. It discusses common machine learning challenges and provides resources for the Azure Machine Learning ecosystem.

1. Sponsors

2. Smart Apps with Azure ML
CHRIS MCHENRY
VP OF TECHNOLOGY, INTEGRO
HTTP://CMCHENRY.COM
@CAMCHENRY

3. “Machine learning is a way of getting
computers to know things when they see
them by producing for themselves the
rules their programmers cannot specify.
The machines do this with heavy-duty
statistical analysis of lots and lots of data.”
“Machine Learning: Field of study
that gives computers the ability to
learn without being explicitly
programmed.”
Arthur Samuel (1959)
“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.”
Tom Mitchell (1998)
“A breakthrough in Machine
Learning would be worth
ten Microsoft’s”
Bill Gates

5. ML Examples
FROM THE PRESS
Spam Filtering
Google/Bing Ad Targeting
Postal Service Mail Sorting
Cortana
Amazon/Netflix Recommendations
Credit Card Fraud Detection
Deep Blue/Watson
How-Old.net
BUSINESS APPS SMART APPS
Automated Workflow Routing
Automated Filing
User Suggestions
Customers Likely to Buy
Customers Likely to Leave
Product Pricing
Order Anomalies

6. Applied ML – Skills Needed
BYOD
◦ Bring Your Own Development skills
◦ REST
Data Processing/Cleansing
◦ SQL/NoSQL
◦ R and/or Python
◦ Hadoop/HD Insight/Azure Stream Analytics
The Right Attitude
◦ Persistence and confidence to understand a complex subject
◦ Unbridled curiosity to explore and iterate and possibly fail
◦ Creativity to find alternatives when you are blocked

7. Process

8. ML Studio
Workspace
Experiment - Modules
◦ Training
◦ Scoring
DataSet
◦ Direct Upload – 10GB Limit
◦ Reader – Azure Blob, Web Page, Odata, SQL Azure, Hive, etc
◦ R or Python Module
Web Services

9. Regression

10. Classification

11. Clustering

13. Demo
1. Create a Training Experiment – Select a Model
2. Create a Scoring Experiment – Prep Selected Model for Runtime
3. Publish as a Web Service – Operationalize a Web Service
4. Consume a Web Service – Get Predictions from your App

14. Common ML Challenges
UNDERFITTING - BIAS OVERFITTING - VARIANCE
1. Add more features
2. Generate features
3. Evaluate training data
1. Reduce features – dimensionality
reduction
2. Add more training data
3. Evaluate training data

15. Ecosystem
Site/ML Studio/Docs: http://azure.microsoft.com/en-us/services/machine-learning/
Gallery: http://gallery.azureml.net/
Azure Marketplace: http://datamarket.azure.com/browse/data?category=machine-learning
Blog: http://blogs.technet.com/b/machinelearning/
Forum: https://social.msdn.microsoft.com/Forums/azure/en-US/home?forum=MachineLearning
Stack Overflow: http://stackoverflow.com/questions/tagged/azure-ml
Webinars: https://azureinfo.microsoft.com/BigDataAdvancedAnalyticsWebinars.html

16. Books
Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable
Solutions in Minutes– Barga, Tok, and Fontama, Apress, 2014
Azure Machine Learning – Jeff Barnes, Microsoft Press, 2015
Data Science in the Cloud with Microsoft Azure Machine Learning and R – Stephen Elston,
O’Reilly, 2015

17. Questions
Contact Info:
cmchenry@Integro.com
@CAMCHENRY
http://cmchenry.com
http://www.linkedin.com/in/cmchenry
https://plus.google.com/+chrismchenry