Reduce truck rolls by 14% and save $10M annually with AI
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This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/1KQTTxMqelg
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Reduce truck rolls by 14% and save $10M annually with AI
- 2. Agenda Truck Roll reduction AI Use case Continuous Development and Deployment of AI Model Data Scientist Driverless AI GAIA Demo
- 3. Opportunity Identified about 14% of truck rolls were resulted ‘No Fault found’, in the system over 1 year. Minimum of 5% of these TRs can be avoided Annual potential savings of about $ 10M • From existing dataset performed n-gram analysis on technician comments to identify meaningful sentences like - • “no fault found”, “clear dial tone”, “localized high workload”, “line ok lead”, “line tested ok”, “fast test pass” etc. • From testing and diagnosis dataset identify first round of test results where successful Data Analysis Approach 2 “COMPLETED no fault found on network working on the pillar line ok tested at customer premise fast test ok” “Complete nff tested with customer line sounds clear customer advised issue only happens when raining intermittent issue” “COMPLETED no fault found in network customer is 4 8 km from exchange and getting 3 85 mbps speed FFS” “COMPLETED dial tone at house pair gain system working no fault found ” Sample Customer Technician Closure Comments Test & Diagnosis Technician Comments Network Health & Alarms Remotely closable Tickets Network Topology Truck Roll reduction Use case : Remotely Closable Tickets 1 3 4 Customer Profile Agent Profile
- 4. AI Model Development Life Cycle
- 5. Data Preparation : Candidate Input Data Sources for TR Prediction Input to AI/ML Model Ticket ProfileCustomer Customer Profile (N/W) Agent Behavior Test & Diagnostics Network Health
- 6. Training the Model : Machine Learning Automation - H2O Driverless AI Driverless AI Expert Data Scientist in a Box Delivers Insights and Interpretability Automatic feature engineering Automatic scoring pipeline Automatic Visualization Flexibility of data & deployment NLP with Tensorflow No code AI/ML
- 7. Training the Model : Truck Roll Prediction Demo Domain: Service Assurance Scope: Predict avoidable Truck Roll vs Valid Truck Rolls. Driverless AI 1 Month Tickets data Test Data Train Data Compare Predicted Vs Actual Values
- 8. AI Development Service and tools Executable Predictor Data Sources Training Dataset Training / Testing Lifecycle Runtime Systems Create & On-Board Models Execute InTarget Environment Local Learning Train Deploy Model Docker Images Onboard Publish Review Search Chaining Sharing Models In Marketplace Rating AI Platform & Marketplace Enhancing ModelWith Application Data Sets Continuous Learning Design Studio Construct chained AI applications GAIA : Platform Overview
- 9. Demo: Output Summary (sample) Actual vs Predicted Matrix – Test Dataset (48k) Summary: • ML model used is XGBoostModel by H20.ai • Training dataset contains historical data • Test dataset used to validate • The accuracy (test dataset) is : ~92 % (higher is better) • Driverless AI performed the various steps(ID-SP) to find the optimal final model, depicted in picture • Driverless AI has come up with a final model in 32 iterations. Steps performed to reach to final Model Predicted : No Truck Roll Predicted : Truck Roll Actual : No Truck Roll 93% 7% Actual : Truck Roll 9% 91%