AIIA - Charting the Path to Intelligent Operations with Machine Learning - Atakan Cetinsoy
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Transcript of AIIA - Charting the Path to Intelligent Operations with Machine Learning - Atakan Cetinsoy
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Charting the Path to Intelligent Operations with Machine Learning
Atakan Cetinsoy VP - Predictive Applications
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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
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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
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The Connected World
We’re here!
SOURCE: Cisco
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The Industrial Internet
SOURCE: General Electric
• Hypothetical 1% efficiency gain via IoT technology.
Savi
ngs
(in B
illion
s U
SD)
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Sensor Data and Predictive Apps
SOURCE: Forrester
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SOURCE: Joseph Sirosh
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Case Study: Digital Cows
SOURCE: Fujitsu.com
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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
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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.
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Data Driven Decision Making
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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.
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Machine Learning?
• “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” — Prof. Arthur Samuel
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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!
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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.
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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
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Future of Machine Learning
• “Machine Learning is becoming a new abstraction layer of the computing infrastructure.”
Tushar Chandra, Principal Engineer — Google Research
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BigMLAn 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
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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
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Interpretable Predictive Models
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Model Variable Contribution
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Model Evaluation
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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