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Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017


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This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.

Published in: Data & Analytics
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Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017

  1. 1. AI AUTOMATION Enabling the AI-Driven Enterprise Rajiv Shah DataRobot
  2. 2. AI Importance Artisan AI Automated AI Demo
  3. 3. AI-Driven Business
  4. 4. AI industry will grow to $70 billion by 2020 from just $8.2 billion in 2013, according to IDC research.
  5. 5. “If your competitor is rushing to build AI and you don't, it will crush you.” -Elon Musk
  6. 6. Enabling the AI-Driven Enterprise Where AI is applied in every business process to predict outcomes. The AI-Driven Enterprise adapts to new conditions at incredible speeds and continually self-optimizes based on predicting the future.
  7. 7. THE OPPORTUNITY FOR MACHINE LEARNING IN ANY BUSINESS Banking Insurance Healthcare Media Pharma Telco Retail Government Energy Transportation Marketing Sales Risk Human Resources Logistics Predicting customer Lifetime Value (LTV) Churn Customer segmentation Product mix (best product mix to reduce churn) Cross selling/recommendation algorithms Up selling Channel optimization Discount targeting Responses rates Reactivation likelihood Adwords optimization and ad buying In store traffic patterns Aircraft scheduling Lead prioritization Demand forecasting Pricing Market Basket Inventory management / Dynamic Pricing Promos/upgrades/offers Resume screening Employee churn Training recommendation Talent management Credit Risk Fraud detection Accounts Payable Recovery Anti-money laundering Insurance Claims prediction Readmission Risk Warranty Analytics Claim Prediction Procurement Warehousing Cost Analysis Product life cycle Demand Forecast Assembly Turnover
  8. 8. Everyone is taking advantage of AI, the question is how fast can you go
  9. 9. Artisans: Data Scientists
  10. 10.
  11. 11. THE AI BOTTLENECK: DATA SCIENTISTS Data Scientist Math & Stats Domain Expertise Data Scientist Programming Skills Knowledge of the business Knowledge of the data Ability to write code to gather data Ability to write code to explore/inspect data Ability to write code to manipulate data Ability to write code to extract actionable intel Ability to write code to build models Ability to write code to implement models Foundational statistics Internals of algorithms Practical knowledge and experience Knowing how to interpret and explain models PREREQUISITES
  12. 12. Accelerate the process of researching, testing, and deploying predictive algorithms. Enable more people to help research, test, and deploy predictive algorithms. Unmet demand for Data Science THE PROBLEM KEY Demand for predictive models Supply of data scientists
  13. 13. There are not enough data scientists (and it's not changing anytime soon)
  14. 14. 80 Billion -- 1200 Billion
  15. 15. AUTOMATED MACHINE LEARNING: THE NEW PREREQUISITES Data Scientist Math & Stats Programming Skills Knowledge of the business Knowledge of the data PREREQUISITESDomain Expertise
  16. 16. Automated AI will fill the gap of data scientists
  17. 17. Issues around Automated AI
  18. 18. CONTROL
  19. 19.
  20. 20. Create data science superheroes with Automated AI
  21. 21. ACCURACY
  22. 22. Automated AI is about lots of relevant options - - - not the dogma of AI elitists
  23. 23. OPEN SOURCE
  24. 24. An AI solution should utilize existing open source AI algorithms
  26. 26. How? partial dependence feature importance Why? Reason codes
  27. 27. No Black Boxes! Understand a model at the prediction level
  28. 28. DEPLOYING AI
  29. 29. “don't do it right, do it twice”
  30. 30. Build & deploy should be as similar as possible
  31. 31. Demo of Automated AI
  32. 32. AI AUTOMATION Enabling the AI-Driven Enterprise Rajiv Shah rajcs4