1. Azure Machine Learning Introduction to Azure ML

2. Setting Expectations This presentation is for you if…  you hear the buzzword “Machine Learning” and want a better understanding of what it is  you want to know how to get started building your first experiment using Azure ML Studio This presentation is NOT for you if…  you already completed Microsoft Virtual Academy and Quick Start offerings related to Azure ML  you already created and published your own machine learning projects

3. Agenda  Why Machine Learning  What is Machine Learning  Machine Learning in Azure  Hands On: Azure ML Studio  Questions

4. Agenda  Why Machine Learning  What is Machine Learning  Machine Learning in Azure  Hands On: Azure ML Studio  Questions

5. The United States Postal Service processed over 150 billion pieces of mail in 2013—far too much for efficient human sorting. But as recently as 1997, only 10% of hand-addressed mail was successfully sorted automatically.

6. The challenge in automation is enabling computers to interpret endless variation in handwriting.

7. More than just mail circulating…

8. Agenda  Why Machine Learning  What is Machine Learning  Machine Learning in Azure  Hands On: Azure ML Studio  Questions

9. Imagine what machine learning could do for your business. Churn analysis Equipment monitoring Spam filtering Ad targeting Recommendations Fraud detection Image detection & classification Forecasting Anomaly detection Using past data to predict the future

10. Imagine what machine learning could do for your business. Churn analysis Equipment monitoring Spam filtering Ad targeting Recommendations Fraud detection Image detection & classification Forecasting Anomaly detection Using past data to predict the future XBOX Halo

11. Imagine what machine learning could do for your business. Churn analysis Equipment monitoring Spam filtering Ad targeting Recommendations Fraud detection Image detection & classification Forecasting Anomaly detection Using past data to predict the future Shopping Basket Analysis

12. Imagine what machine learning could do for your business. Churn analysis Equipment monitoring Spam filtering Ad targeting Recommendations Fraud detection Image detection & classification Forecasting Anomaly detection Using past data to predict the future Credit Card

13. Imagine what machine learning could do for your business. Churn analysis Equipment monitoring Spam filtering Ad targeting Recommendations Fraud detection Image detection & classification Forecasting Anomaly detection Using past data to predict the future Health / Medical

14. Techniques for solving? ClassificationRegressionClustering (Recommenders) Anomaly Detection

15. Types of machine learning  Supervised Learning  Used when you want to find unknown answers and have data with known answers  Train model using test data with known outcomes  Measure effectiveness of various algorithms’ prediction against known outcomes in test set  Publish best trained model to predict outcomes for new inputs  Unsupervised Learning  Used when you want to find unknown answers – mostly groupings – directly from data  No simple way to evaluate accuracy  Apply algorithm  Evaluate groups

16. Sample – Supervised Learning Start with a question: Which customers will buy a bike?

17. Sample – Supervised Learning Analyze historical data set that includes predictive attributes and known answer. 2 kids 5 mile commute =

18. Sample – Supervised Learning Analyze historical data set that includes predictive attributes and known answer. 1 kid 15 mile commute =

19. Separate into Training and Test sets Training Test

20. Agenda  Why Machine Learning  What is Machine Learning  Machine Learning in Azure  Hands On: Azure ML Studio  Questions

21. Challenges prior to Azure ML Hard-to-reach solutions Huge set-up costs of tools, expertise, and compute/storage capacity create unnecessary barriers to entry Siloed and cumbersome data management restricts access to data Complex and fragmented tools limit participation in exploring data and building models Many models never achieve business value due to difficulties with deploying to production Expensive Siloed data Fragmented tools Deployment complexity Break away from industry limitations

22. Introducing Azure Machine Learning Accessible solutions Minimal set-up costs with ability to easily scale compute/storage capacity; fewer barriers to entry Easy to integrate data from various data sources Users can collaborate in common toolset to build and train models using advanced algorithms (also supports existing R and Python assets) Easy to deploy trained models as consumable web services Cloud- based Data Integration Common Toolset Deployment simplicity

23. Agenda  Why Machine Learning  What is Machine Learning  Machine Learning in Azure  Hands On: Azure ML Studio  Questions

24. http://azure.microsoft.com/en-us/services/machine-learning/

25. Steps to build a ML Solution

26. Agenda  Why Machine Learning  What is Machine Learning  Machine Learning in Azure  Hands On: Azure ML Studio  Questions

27. Contact Info  Scott Hietpas  Shietpas@SkylineTechnologies.com Shietpas@SkylineTechnologies.com  http://www.linkedin.com/pub/scott- hietpas/2/119/189 http://www.linkedin.com/pub/scott- hietpas/2/119/189  Adam Widi  Awidi@Skylinetechnologies.com Awidi@Skylinetechnologies.com  http://www.linkedin.com/pub/adam- widi/15/5aa/499 http://www.linkedin.com/pub/adam- widi/15/5aa/499