Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Sponsors
Smart Apps with Azure ML
CHRIS MCHENRY
VP OF TECHNOLOGY, INTEGRO
HTTP://CMCHENRY.COM
@CAMCHENRY
“Machine learning is a way of getting
computers to know things when they see
them by producing for themselves the
rules th...
ML Examples
FROM THE PRESS
Spam Filtering
Google/Bing Ad Targeting
Postal Service Mail Sorting
Cortana
Amazon/Netflix Reco...
Applied ML – Skills Needed
BYOD
◦ Bring Your Own Development skills
◦ REST
Data Processing/Cleansing
◦ SQL/NoSQL
◦ R and/o...
Process
ML Studio
Workspace
Experiment - Modules
◦ Training
◦ Scoring
DataSet
◦ Direct Upload – 10GB Limit
◦ Reader – Azure Blob, ...
Regression
Classification
Clustering
Demo
1. Create a Training Experiment – Select a Model
2. Create a Scoring Experiment – Prep Selected Model for Runtime
3. ...
Common ML Challenges
UNDERFITTING - BIAS OVERFITTING - VARIANCE
1. Add more features
2. Generate features
3. Evaluate trai...
Ecosystem
Site/ML Studio/Docs: http://azure.microsoft.com/en-us/services/machine-learning/
Gallery: http://gallery.azureml...
Books
Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable
Solutions in Minutes– Barga,...
Questions
Contact Info:
cmchenry@Integro.com
@CAMCHENRY
http://cmchenry.com
http://www.linkedin.com/in/cmchenry
https://pl...
Denver Dev Day - Smart Apps with Azure ML
Denver Dev Day - Smart Apps with Azure ML
Upcoming SlideShare
Loading in …5
×

Denver Dev Day - Smart Apps with Azure ML

395 views

Published on

I recently presented at the Denver Dev Day on Smart Apps with Azure ML: In the words of Marc Andreessen, "Software is eating the world". Industries are being disrupted at an alarming rate due to intelligent software. Azure Machine Learning enables developers to easily add intelligence to their Apps. In this session we'll look at the recently GA'd Azure ML service and see how it's easy to make your Apps smart!

Published in: Software
  • Be the first to comment

  • Be the first to like this

Denver Dev Day - Smart Apps with Azure ML

  1. 1. Sponsors
  2. 2. Smart Apps with Azure ML CHRIS MCHENRY VP OF TECHNOLOGY, INTEGRO HTTP://CMCHENRY.COM @CAMCHENRY
  3. 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
  4. 4. 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
  5. 5. 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
  6. 6. Process
  7. 7. 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
  8. 8. Regression
  9. 9. Classification
  10. 10. Clustering
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. Questions Contact Info: cmchenry@Integro.com @CAMCHENRY http://cmchenry.com http://www.linkedin.com/in/cmchenry https://plus.google.com/+chrismchenry

×