Leveraging the Power of Machine Learning at GE | AnacondaCON 2017
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Transcript of Leveraging the Power of Machine Learning at GE | AnacondaCON 2017
Leveraging the Power of Machine Learning at GE
Girish Modgil
Leveraging the Power of Machine Learning at GE 2© 2016-2017 General Electric Company. All rights reserved.
GE Power
Leveraging the Power of Machine Learning at GE 3
Topics for Discussion
• General Electric – A Digital Industrial Company
• Predix
• Data Science Platform (Why?-What?-How?)
• Machine Learning at GE: A Business Example
GE: A DIGITAL INDUSTRIAL COMPANY300,000+ people operating in 175 countries
Revenue 2015
~$21B ~$8B ~$6B $16B $25B $6B $18B $9B
POWER ENERGY CONNECTIONS
RENEW. OIL & GAS AVIATION TRANS. HEALTHCARE
APPL. & LIGHT
4Leveraging the Power of Machine Learning at GE
Leveraging the Power of Machine Learning at GE 5
Digital Industrial for Operational Excellence
Asset-centric industries have the most to gain from the next wave of disruptive digital innovation
Systems of Record
Systems of Assets
Systems of Engagement
Optimized Digital
Business
Financials,HR, Inventory, Purchasing,
Supply Chain, etc.
Smart connected products, asset
performance mgmt, 0perations 0ptimization,
etc.
Real-time collaboration, Social Media, Mobile, etc.
Leveraging the Power of Machine Learning at GE 6
GE’s Digital Industrial Journey
Leveraging the Power of Machine Learning at GE 7
Transforming Industrial Operations
Asset Performance Management
Operations Optimization Digital Twin/Digital Thread
Maximize performance and asset availability
Increase system efficiency across operations
Optimize lifecycle of design, manufacturing, service,
& repair cycles
CreatingNew Value
Improved operational performance and efficiency
1
New customer services and business models2
Continuous innovation and faster time to market3
Predix: Machine to Cloud Platform as-a-Service
8Leveraging the Power of Machine Learning at GE
Leveraging the Power of Machine Learning at GE 9
Industrial Analytics
Physics-basedAdvanced Data Science Applied Engineering
DataContinuous, accessible
StatisticsIdentify trends and anomalies
PhysicsApply asset and domain
expertise
Industrial Outcomes
One platform for OT and IT teams to collaborate and
innovate+ + =
Operator A
Operator B CruiseTakeoff
Tem
pera
ture
Threshold
Clim bPart Temperature
Leveraging the Power of Machine Learning at GE 10
Open Data Science Platform
Need for a modern analytics platform (’Why?’):
• Global Teams: U.S. , India, Switzerland
• Real-Time Collaboration
• Simplify Administration
• Common ML development platform
• Consistent Methods
Leveraging the Power of Machine Learning at GE 11
Open Data Science Platform
One of our Analytics Platforms (‘What?’):
• 50 users
• HWX cluster: AEN compute node + Cluster Head Node
• Hue (Edge Node), Hive (HWX Cluster)
• Anaconda, R, Spark, Hadoop
Leveraging the Power of Machine Learning at GE 12
Open Data Science Platform
Implementation (‘How?’):
• Digital Industrial Strategy
• Tightly integrated teams
• Software Centers of Excellence (CoE)
• Free exchange of ideas across GE businesses
• “Everyone will know how to Code” – Jeff Immelt, Aug 2016
• Predix/GE Store/Digital Twin
Leveraging the Power of Machine Learning at GE 13
Machine Learning: Resource Forecasting
• Outage Field Engineer (TFA)• Predict number of field resources needed by region and specialty
• Life Extension Service (LES)• Predict number of hours needed by region and skill
• Gas/Steam/Generator On-site Repair (OSR)• Predict number of hours needed by region and skill
Objective:Ø Identify factors driving the resource requirement Ø Build data science models to predict resource needs
• Outage Field Engineer (TFA)• Predict number of field resources needed by region and specialty
• Life Extension Service (LES)• Predict number of hours needed by region and skill
• Gas/Steam/Generator On-site Repair (OSR)• Predict number of hours needed by region and skill
Leveraging the Power of Machine Learning at GE 14
Machine Learning: Resource Forecasting
• Built linear regression/time series models based on historical timesheets, seasonality, and outage counts; forecast based on future outage schedule
• Previous SME models over estimated whereas data science models better match the actuals
• Manual• SME
Old
• Automated• Data Driven
New
Leveraging the Power of Machine Learning at GE 15
Machine Learning: Resource Forecasting
• Cloud based analytic
• Results published to big data platform
• Minimal requirement on users (upload, run, post-process)
• Operationalized and in-use
• Shared across GE businesses via GE Store
Tools
5 minutes end-to-end processing time