Presentation given at ODSC London 2016 https://www.odsc.com/london

1. Predicting the
future as a service
with Azure ML and R
Barbara Fusinska
@BasiaFusinska

2. About me
Programmer
Machine Learning
Data Solutions Architect
@BasiaFusinska

3. Agenda
• Machine Learning
• Supervised vs unsupervised learning
• Building blocks
• Azure ML
• Classification/Regression processes
• Training vs Testing
• Building a solution
• Using R code
• Learning from the community

4. Machine Learning?

6. Movies Genres
Title # Kisses # Kicks Genre
Taken 3 47 Action
Love story 24 2 Romance
P.S. I love you 17 3 Romance
Rush hours 5 51 Action
Bad boys 7 42 Action
Question:
What is the genre of
Gone with the wind
?

7. Data-based classification
Id Feature 1 Feature 2 Class
1. 3 47 A
2. 24 2 B
3. 17 3 B
4. 5 51 A
5. 7 42 A
Question:
What is the class of the entry
with the following features:
F1: 31, F2: 4
?

8. Data Visualization
0
10
20
30
40
50
60
0 10 20 30 40 50
Rule 1:
If on the left side of the
line then Class = A
Rule 2:
If on the right side of the
line then Class = B
A
B

9. Chick sexing

10. Supervised learning
• Classification, regression
• Label, target value
• Training & Test sets

11. Unsupervised learning
• Clustering, feature selection
• Finding structure of data
• Statistical values describing the data

12. Machine Learning workflow
Data
preparation
Data split
Machine Learning
algorithm
Trained model Score
Clean
data
Training
data
Test data

13. Non-ML components
• Data cleansing & transformation
• Splitting data
• I/O
• Model evaluation
• Comparing algorithms
• Algorithms settings

15. Data -> Predictive model -> Operational web API in minutes
APIML STUDIO

16. Azure ML data sources
• Built in datasets
• Save datasets
• Uploaded data
• Import data module:
• Web URL via HTTP
• Hive Query
• Azure SQL Database
• Azure Table
• Azure Blob Storage
• Data Feed Provider

17. Demo: Income prediction
Simple Machine Learning process
Using built in datasets
Scoring & Evaluation

18. Machine Learning building blocks
• Built-in components
• Datasets
• Algorithms
• Scoring & Evaluation
• Binary classification
• I/O
• Visualization

19. Demo: Automobile price
reduction
Data preparation
Training/Test split
Web Service setup

20. Building a solution
• Regression
• Dataset upload
• Data preparation
• Training vs Testing
• Web service

21. Demo: Credit risk assessment
Saved dataset
Algorithm comparison
Using R

22. Comparing solutions
• Reading data from external
source
• Data normalisation
• Using R scripts

23. Demo: Twitter sentiment analysis
Uploading template
Feature hashing
Feature selection

24. Cortana Intelligence Gallery

25. Using Cortana gallery
• Creating experiment from
template
• Feature selection
• Scoring & evaluation of training
and test data

26. Other Azure ML
capabilities
• History
• Debugging
• Parameters
• Cross-validation
• Retraining model
• Feature selection

28. Thank you
BarbaraFusinska.com
@BasiaFusinska