Denver Dev Day - Smart Apps with Azure ML

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Smart Apps with Azure ML

CHRIS MCHENRYVP OF TECHNOLOGY, INTEGROHTTP://CMCHENRY.COM@CAMCHENRY

“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

ML ExamplesFROM 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

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

Process

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

Regression

Classification

Clustering

Demo1. 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

Common ML ChallengesUNDERFITTING - 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

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

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

Questions Contact Info:

[email protected]

@CAMCHENRY

http://cmchenry.com

http://www.linkedin.com/in/cmchenry

https://plus.google.com/+chrismchenry