Video can be found here: https://vimeo.com/63271828

1. Henrik Brink, CTO
PyData SV 2013, March 19
Machine Learning, Python
and Raspberry Pi’s
@brinkar @wiseio

4. 11kly
11kx
Reference New Difference
Palomar Transient Factory (PTF)
~1.5 million candidates per night
~10 new transients
http://www.astro.caltech.edu/ptf/

5. PTF11kly (SN 2011fe)
©Peter Nugent
Supernova Discovery in the Pinwheel Galaxy
11 hr after explosion
Nearest SN Ia in >3 decades

6. http://bigmacc.info

8. Python Bootcamps at Berkeley

9. Machine Learning in

10. ML pain points I
• Data is messy!
• Hard to scale non-linear algorithms to
large datasets
• Ad-hoc feature engineering
• Collaboration on data, features and
models is difficult
data scientists

12. Random Forest®
Random Forest® is a registered trademark of Salford Systems

13. Brink+2012

14. Brink+2012
10% label noise

15. http://wise.io

17. bit.ly/YZZb9d
WiseRF™

18. WiseRF™
12 GB MNIST in 90 seconds
8 core EC2 instance

19. WiseRF™
A brain for the Internet of Things!

20. Demo™
WiseRF™

21. Higher speeds
Larger datasets
Hadoop / Mahout
Energy sensors
Ad bidding
Credit card
fraud detection
Video tracking
Financial predictions
Batch product
recommendations Real-time
recommendations
Internet of Things
Healthcare
sensors
Fast vs Scalable

22. High-frequency
data science
High-frequency
prediction
Feature engineering
Data ingestion
Model deployment
Model validation
High-frequency Machine Learning

23. • Statistical validation of models
• Lack of feature engineering expertise
• Dealing with data and computing
infrastructure
ML pain points II
application developers

24. Machine Learning as a Service™

25. Machine Learning as a Service™
bit.ly/15eWEZG

26. Reusable features

27. Collaboration

28. Integration

30. docs.wise.io

34. • Scalable infrastructure required
• Hard to go from data science
experiments to production.
• Complete privacy / security.
ML pain points III
business and enterprise

37. @brinkar
@wiseio
henrik@wise.io
We’re hiring!
contact@wise.io