Charting the Path to Intelligent Operations  with Machine Learning

Atakan Cetinsoy VP - Predictive Applications

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

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

The Connected World

We’re here!

SOURCE: Cisco

The Industrial Internet

SOURCE: General Electric

• Hypothetical 1% efficiency gain via IoT technology.

Savi

ngs

(in B

illion

s U

SD)

Sensor Data and Predictive Apps

SOURCE: Forrester

SOURCE: Joseph Sirosh

Case Study: Digital Cows

SOURCE: Fujitsu.com

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

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.

Data Driven Decision Making

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.

Machine Learning?

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

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!

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.

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

Future of Machine Learning

• “Machine Learning is becoming a new abstraction layer of the computing infrastructure.”

Tushar Chandra, Principal Engineer — Google Research

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

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

Interpretable Predictive Models

Model Variable Contribution

Model Evaluation

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

cetinsoy@bigml.com BigMLcom

Q&A