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TRANSCRIPT
Watson / Presentation Title / Date1 #BeBold
2017 Sales Academy#BeBold
Analytics for IoTWillem Hendriks
Arjen van der Wetering
Watson / Analytics for IoT / 27 Feb 20172 #BeBold
1. Machine Learning Demystified (a small intro)
2. Typical IoT Architecture for Analytics
3. How DSX & IoT Platform support the Typical Architecture
4. Use-Case (RAMLAB Welding Robot)
Watson / Analytics for IoT / 27 Feb 20173 #BeBold
Task we want the computer to take over. Examples:- Classification- Regression
“What activity am I doing, based on my measurements?”
walking / driving / sleeping / working / change diapers
“What is the probability, the machine will fail in the next 60 minutes?”
a number from 0% to 100%
Machine Learning Demystified
Watson / Analytics for IoT / 27 Feb 20174 #BeBold
Task we want the computer take over:- Classification- Regression
Data
Watson / Analytics for IoT / 27 Feb 20175 #BeBold
Task we want the computer take over:- Classification- Regression
Data
model / method
learns from data
Watson / Analytics for IoT / 27 Feb 20176 #BeBold
Task we want the computer take over:- Classification- Regression
Data
model / method
learns from data
measurement forquality of our model / method
Watson / Analytics for IoT / 27 Feb 20177 #BeBold
Task we want the computer take over:- Classification- Regression
Data
model / method
learns from data
measurement forquality of our model / method
The machine is training!
Watson / Analytics for IoT / 27 Feb 20178 #BeBold
Data
model
learns from datameasurement forquality of our model
We call columns in our data “features”
We call transforming the columns to help the model learn“feature engineering”
example: speed = distance/time,will possible help the model learn,to classify my activity:
low speed : high chance sleepinghigh speed: high chance driving
Example model for classification:loglogistic regression
Example model for regression:linear model
many many more, tree's, random forests, deeplearning, SVM
Spark, SPSS, python, are tools to train models from data.
DSX is a cloud environment, where many tools are available for analysts.
To evaluate practical usage of our trained model, we want metrics of the learning
“95% of activities correctly classified”
95% can cause deaths, or make you a millionaire in 24 hours
Watson / Analytics for IoT / 27 Feb 20179 #BeBold
Historical Data
Create a collection of historical data.
Typical IoT Architecture for Analytics
Watson / Analytics for IoT / 27 Feb 201710 #BeBold
Historical Data
model
learns from data
Typical IoT Architecture for Analytics
Watson / Analytics for IoT / 27 Feb 201711 #BeBold
Current Architecture
Welding Robot
Watson IOTPlatform
CloudantDatastore
StreamingAnalytics
AnalyticsDashboard
Raw events Raw events
Raw events
Aggregated values
OperatorDashboard
Node-REDapplication
Watson / Analytics for IoT / 27 Feb 201712 #BeBold
Historical Data
model
learns from data
When we are happy about the performance, we can implement the model, and benefit in the IoT world.
Analytical Components in IBM Bluemix
Watson / Analytics for IoT / 27 Feb 201714 #BeBold
Historical Datamodel
learns from data
Cloudant
geo-spatial!
BigSQL!
Real real-time!
Data Scientists!
Bluemix & DSXData Science Experience
Watson / Analytics for IoT / 27 Feb 201716 #BeBold
Use-Case RAMLAB Welding Robot
Watson / Analytics for IoT / 27 Feb 201717 #BeBold
Current Architecture
Welding Robot
Watson IOTPlatform
CloudantDatastore
StreamingAnalytics
AnalyticsDashboard
Raw events Raw events
Raw events
Aggregated values
OperatorDashboard
Node-REDapplication
Watson / Analytics for IoT / 27 Feb 201718 #BeBold
Enhanced Architecture
Watson IOTPlatform
CloudantDatastore
StreamingAnalytics
AnalyticsDashboard
Raw events
Raw events
Aggregated values
OperatorDashboard
Other sources
Feedback loop(predicted errors, control)
Data Science Experience
Watson / Analytics for IoT / 27 Feb 201719 #BeBold
DEMO