The Internet of Things is about data, not things: Some forecasts that by 2018 the number of connect things will exceed the combined number of personal computers, smartphones, and tablets. Each ’thing’ can produce a tremendous stream of data from sensors and other sources. This presentation will discuss progress, examples, challenges, and opportunities with machine learning for the IoT. A short presentation will be done on some recent applications of ML (using H2O) to the domains of machine prognostics / health management (PHM) and agriculture.

1. H2O.ai
Machine Intelligence
ML is the new SQL
Prediction is the new Search

2. H2O.ai
Machine Intelligence
Machine Learning
for the
Sensored Internet of Things
Hank Roark
hank@h2o.ai
@hankroark
2

3. H2O.ai
Machine Intelligence
Who am I?
▪ Data Scientist & Hacker @ H2O.ai
▪ Lecturer in Systems Thinking, University of Illinois at Urbana-Champaign
▪ John Deere, Research, Software Product Development, High Tech Ventures
▪ Lots of time dealing with data off of machines, equipment, satellites, radar,
hand sampled, and on.
▪ Geospatial and temporal / time series data almost all from sensors.
▪ Previously at startups and consulting (Red Sky Interactive, Nuforia,
NetExplorer, Perot Systems, a few of my own)
▪ Systems Design & Management MIT
▪ Physics Georgia Tech

4. H2O.ai
Machine Intelligence
IoT Data Comes From Lots of Places,
Much of it from Sensors

5. H2O.ai
Machine Intelligence
The data is going to be huge, so get ready

6. H2O.ai
Machine Intelligence
Wow, how big is a brontobyte?

7. H2O.ai
Machine Intelligence
This much data will require a fast OODA loop
Much of these models will then be used in control systems
Image courtesy http://www.telecom-cloud.net/wp-content/uploads/2015/05/Screen-Shot-2015-05-27-at-3.51.47-PM.png

8. H2O.ai
Machine Intelligence
Machine Prognostics Use Case
Sensor data of turbofan remaining useful life prediction
Jupyter notebook @ https://goo.gl/G2zx3o
Many more tips and tricks

9. H2O.ai
Machine Intelligence
Key take aways for modeling the sensored IoT
• Some sort of signal processing is usually helpful, but can introduce bias
• Smoothers, filters, frequency domain, interpolation, LOWESS, ... , feature
engineering
• Validation strategy is important
• Easy to memorize due to autocorrelation
• Sometimes the simplest things work
• Treat each observation independently; Use time, location, as data elements
• Uncertainty is the name of the game
• Methods that will report out probabilities are often required (not shown here)
• The data can be big, get ready, it'll be a great ride
• Scalable tools like H2O will help you model the coming bronobytes of data