Slides of Atakan Cetinsoy's presentation at the AIIA conference at Melbourne (March, 2015)

1. Charting the Path to
Intelligent Operations
with Machine Learning
Atakan Cetinsoy
VP - Predictive Applications

2. 21st Century Megatrends
As the world population is headed to
10 billion:
• Intensifying scramble for scarce
resources
• Growing urbanization and diversity
• Social media and the shifting
balance of power
SUSTAINABILITY
PRODUCTIVITY
ENGAGEMENT

3. Utility Industry Trends
• Evolving energy portfolio
• Transition to distributed generation
schemes
• Efficiency as a “New” energy resource
• Growing smart meter infrastructure
• Dynamic pricing and demand response

4. The Connected World
We’re
here!
SOURCE: Cisco

5. The Industrial Internet
SOURCE: General Electric
• Hypothetical 1% efficiency gain via IoT technology.
Savings(inBillionsUSD)

6. Sensor Data and Predictive Apps
SOURCE: Forrester

7. SOURCE: Joseph Sirosh

8. Case Study: Digital Cows
SOURCE: Fujitsu.com

9. IoT Time Series Data
Sensor Time +7 +35 +50 BLOB
101 15:00 N/A N/A N/A {…}
102 15:00 N/A N/A N/A {…}
102 15:01 N/A N/A N/A {…}
103 15:01 11 20 N/A {…}
103 15:02 N/A N/A 33 {…}
1 Minute
Time Window
Offset in
Seconds
• Wide row structure with possibly 1000s of measurements
• 100M to 1 billion data points per second can be processed!
• Compacted into BLOB format stored as a single value
SOURCE: MapR

10. Big Data or Big Hype?
• Data that is
• Too big to fit on a single server
• Too unstructured to fit into
rows and columns
• Too continuos to fit into an
EDW
• “Size matters” but actionable
insights take the prize.

11. Data Driven Decision Making

12. Evolution of Analytics
Attribute Traditional Analytics Analytics 2.0
Data Type Rows and Columns Unstructured
Volume Up to TBs Up to PBs
Flow Static Pool Continuos
Technology EDW + SQL
Open Source +
Machine Learning
Analysis
Descriptive,
Hypothesis-based
Predictive,
Machine Learned
Purpose
Internal Decision
Support
Data-driven Products/
Services
SOURCE: Thomas H. Davenport
Includes everything in
Traditional Analytics
plus the following.

13. Machine Learning?
• “Machine Learning is the field of study that gives
computers the ability to learn without being explicitly
programmed.” — Prof. Arthur Samuel

14. The Need for Machine Learning
• Can you find any pattern in this tiny data set?
• Now imagine millions of rows and thousands of
columns of it!

15. The Need for Data-driven Decisions
• Human intuition is poor
• Human judgement is biased
• Human reasoning is causal and
not statistical
• Machine Learning is a tool to help
people make smarter, unbiased,
more effective data-driven
decisions.

16. What is a Data Scientist?
Industry
Subject-matter Expertise
Computer Science
and/or Hacking Skills
Math and Statistics
Knowledge
Machine
Learning
Traditional
Research
Data
Science
SOURCE: Drew Conway

17. Future of Machine Learning
• “Machine Learning is
becoming a new
abstraction layer of the
computing infrastructure.”
Tushar Chandra,
Principal Engineer
— Google Research

18. BigML
An end-to-end machine learning
platform that is
• Builds interpretable machine
learning models that address
the vast majority of predictive
tasks.
• Accessible to the entire
organization to make data-
driven decisions.
• Provides a public API so that
application developers can build
predictive applications.
• Cloud-born solution that
provides instant access and
instant scale.
CONSUMABLE
PROGRAMMABLE
SCALABLE

19. Predictive Modeling Best Practices
• Business objective and
predictive model alignment
• Proof of concept based on
sampled data
• Model validation with proper
accuracy measures
• Transparent vs. “Black Box”
algorithms

20. Interpretable Predictive Models

21. Model Variable Contribution

22. Model Evaluation

23. Predictive Apps for Utilities
• Operational
• Accurate and Granular Load Forecasting
• Network Outage Predictions
• System Failure Predictions
• Demand Response Optimization
• Marketing
• Customer Churn Prediction
• Pricing Response Prediction
• Energy Efficiency
• Household Level Predictive Analytics

24. cetinsoy@bigml.com
BigMLcom
Q&A