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AI Orange Belt - Day 1 - case by Jetpack.ai

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AI Orange Belt - Day 1 - case by Jetpack.ai

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AI Orange Belt - Day 1 - case by Jetpack.ai

  1. 1. An AI project from A to Z AI Orange Belt
  2. 2. © Copyright 2019 - Jetpack SPRL
  3. 3. DATA ENGINEERING DATA MINING / MACHINE LEARNING VISUALISATION / UI Data Science Toolbox DOMAIN / BUSINESS KNOWLEDGE
  4. 4. © Copyright 2019 - Jetpack SPRL Jetpack.AI accelerates their analytics journey Customer segmentation tool Profiling and campaigning segmentation Truck & trailers IoT Fuel and usage efficiency Store Manager (Mobile App) Real-time sales performance and issue alerting Machine Learning for issue identification Issue prediction and best resolution identification Train maintenance and failure analysis Promotions optimization Promo & loyalty impact on traffic, sales & margin
  5. 5. Could we predict which type of technician we need to send to fix our customer’s issue ? “ “
  6. 6. Automated technician routing prevents useless interventions Customer complains Send generalist technician Send specialised technician $ $$$$ Diagnostic + intervention Handover (xx% precision) Direct prediction by Jetpack’s algorithm (YY% precision)
  7. 7. Automated technician routing: a typical AI project Data collection & cleaning Data enrichment Model training & validation Automation
  8. 8. Automated technician routing: a typical AI project Data collection & cleaning Data enrichment Model training & validation Automation Problem definition & solution ideation Use case validation
  9. 9. Automated technician routing: a typical AI project Data collection & cleaning Data enrichment Model training & validation Automation Problem definition & solution ideation Use case validation
  10. 10. Data is the oil of your AI project, so you better ensure a smooth access to it
  11. 11. Data collection and cleansing takes a large part of the project Electric measurements Past interventions Numeric signal quality Technician tests after past interventions Network structure Reconciliation between client ID and line number Construction of network architecture Filtering on intervention codes Translation of electric measures to cable degradation level Impact of network correlations Intervention codes
  12. 12. Data collection can quickly become a technical challenge in historical companies MySQL Teradata Oracle Hive (Big Data) HBase (Big Data) Cleansing Formating Unification Business logic encoding
  13. 13. Automated technician routing: a typical AI project Data collection & cleaning Data enrichment Model training & validation Automation Problem definition & solution ideation Use case validation
  14. 14. Data enrichment gets us past sums and averages Detection of frequent sequences of events leading to specific defaults allowed to grasp more complex interactions between events
  15. 15. Investing in data enrichment is often a gamble Expected value added by feature Expected effort to develop feature
  16. 16. Automated technician routing: a typical AI project Data collection & cleaning Data enrichment Model training & validation Automation Problem definition & solution ideation Use case validation
  17. 17. We trained a model for binary classification Gradient Boosting Machine: train multiple weak decision trees iteratively, then combine their predictions X y X y Original data Training set Test set Split X X y y Validation set
  18. 18. The outcome of a model is of probabilistic nature
  19. 19. Choosing the final parameters will depend on the business case of the project
  20. 20. Automated technician routing: a typical AI project Data collection & cleaning Data enrichment Model training & validation Automation Problem definition & solution ideation Use case validation
  21. 21. Lazy execution, version compatibilities, context awareness... Do not worry about your difficulties with computers. I can assure you mine are still greater.
  22. 22. Go or no go? The decision is based on the ROI Customer complains Send generalist technician Send specialised technician 100€ 500€ Diagnostic + intervention 15% handover (90% precision)
  23. 23. Go or no go? The decision is based on the ROI Customer complains Send generalist technician Send specialised technician 100€ 500€ Diagnostic + intervention 15% handover (90% precision) Direct prediction by Jetpack’s algorithm (X% precision)
  24. 24. The maximal case caps the possible gains First question: out of 1000 cases, what is the maximal expected gain? - Typical set-up: 100.000€ diagnostic + 75.000€ expert time - AI optimal set-up: 850*100€ + 150*0.1*100€ + 150*0.9*500€ = 154.000€ Maximal expected gain: 21.000€ for 1000 cases, so 21€ per case. A typical year would see 100.000 cases, so the maximal possible business case is 2.1M€
  25. 25. The exact case depends on which parameters are decided Customer complains Send generalist technician Send specialised technician 100€ 500€
  26. 26. The exact case depends on which parameters are decided Customer complains Send generalist technician Send specialised technician 100€ 500€ Gain 100€
  27. 27. The exact case depends on which parameters are decided Customer complains Send generalist technician Send specialised technician 100€ 500€ Gain 100€ No gain
  28. 28. The exact case depends on which parameters are decided Customer complains Send generalist technician Send specialised technician 100€ 500€ Gain 100€ No gain Loss 400€
  29. 29. Simplified business case Gains = TP*100 - FP*400 TP/FP > 400/100 98/17 = 5.76 ⇒ positive gains But, only 84.5% of cases captured Can we do better?
  30. 30. Simplified business case Now we capture 98.3% of cases! But, TP/FP = 3, negative gains! Need to find the optimal threshold, maximizing the size of business case + Taking into account non-monetary gains (customer satisfaction, team workload desaturation…)
  31. 31. Diagnostic + intervention After launch, keep model up to date and don’t forget maintenance Customer complains Send generalist technician Send specialised technician $ $$$$ Direct prediction by Jetpack’s algorithm Running now for > 2 years, models are re-trained every 3-4 months. Maintenance is often underestimated but necessary because of evolution of data environment in the company and shifting priorities
  32. 32. Take-away messages ● Don’t underestimate the importance of data access (most common reason of failure of AI projects) ● Data science is always (partly) research ● Outcomes are never black & white, you’ll need to learn to deal with probabilistic results ● Operational AI requires a LOT of communication with teams. Better start early in the project
  33. 33. © Copyright 2019 - Jetpack SPRL Thank you! Gautier Krings gautier@jetpack.ai

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