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.

1. AI AUTOMATION
Enabling the AI-Driven Enterprise
Rajiv Shah
DataRobot

2. AI Importance
Artisan AI
Automated AI
Demo

3. AI-Driven Business

4. AI industry will grow to $70 billion by 2020
from just $8.2 billion in 2013, according
to IDC research.

5. “If your competitor is rushing to build AI and
you don't, it will crush you.”
-Elon Musk

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. 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. Everyone is taking advantage of
AI, the question is how fast can
you go

9. Artisans: Data Scientists

10. http://www.eyeonsouthflorida.com/wp-content/uploads/2016/08/artisan3.jpg

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. 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. There are not enough data
scientists (and it's not changing
anytime soon)

15. 80 Billion -- 1200 Billion

16. AUTOMATED MACHINE LEARNING: THE NEW PREREQUISITES
Data
Scientist
Math &
Stats
Programming
Skills
Knowledge of the business
Knowledge of the data
PREREQUISITESDomain
Expertise

17. Automated AI will fill
the gap of data scientists

18. Issues around Automated AI

19. CONTROL

20. http://farm3.static.flickr.com/2315/5702627963_36f5015261.jpg

23. Create data science superheroes
with Automated AI

24. ACCURACY

28. Automated AI is about lots of
relevant options - - - not the
dogma of AI elitists

29. OPEN SOURCE

31. An AI solution should utilize
existing open source AI
algorithms

32. INTERPRETABILITY

34. How?
partial dependence
feature importance
Why?
Reason codes

35. No Black Boxes!
Understand a model at the
prediction level

36. DEPLOYING AI

37. “don't do it right, do it twice”

38. Build & deploy should be as
similar as possible

39. Demo of Automated AI

40. AI AUTOMATION
Enabling the AI-Driven Enterprise
Rajiv Shah
rajiv.shah@datarobot.com
rajcs4