The document discusses how machine learning and automation can help address the shortage of data scientists and the demand for predictive models. It presents DataRobot as a solution that can turn business analysts into data scientists by automating predictive modeling. DataRobot allows users of all skill levels to build and deploy models, and makes data scientists more productive by reducing the need for coding. It can scale to handle large volumes of data and models.

1. Where are my predictions?
I needed them yesterday!
(A recipe for Automated Data Science)
Presented to you by eRic Choo

2. Maths &
Stats
Domain
Knowledge
Hacking
Skills
Data
Science

12. Source: http://blogs.sas.com/content/subconsciousmusings/2014/08/22/looking-backwards-looking-forwards-sas-data-mining-and-machine-learning/

13. Source: http://blogs.sas.com/content/subconsciousmusings/2014/08/22/looking-backwards-looking-forwards-sas-data-mining-and-machine-learning/

14. Source: http://blogs.sas.com/content/subconsciousmusings/2014/08/22/looking-backwards-looking-forwards-sas-data-mining-and-machine-learning/

15. =BIGD A T A
BIGW O R K L O A D?

16. Before:
• books were hand
written
• Scriptoria in
Monasteries where
monks copy
manuscripts
• Books (and
knowledge) scarce

17. Today:
• Process automated by
printing press (invented
in 1440
• > 20,000,000 volumes
were produced in 1500
• Books (and knowledge)
accessible

18. The OLD way:
• Applying complex mathematical
theories to experimental data
• strictly controlled, expensive
experiments hence Data limited
• laboriously designed and
constructed via manual process

19. The NEW way:
• Computers collect/store vast
amount of information, and do
mathematics faster and cheaper
• Machine learning, computers
automatically learn from data
• Data insights and knowledge

21. Statistics:
• Hypothesis testing
• Exist before a data
is seen
• Theory/Idea

22. Speed
Machine
Learning:
• Pattern finding
and extrapolation
• found in the data
only after analysis
commences

23. Statistics:
• Further understand a clearly
defined hypothesis test
• Low volume, structured
data collected from
controlled experiments

24. Machine
Learning:
• Predict future events
and/or automate
decisions
• High volume,
unstructured data
collected from real life

36. Source: http://www.australianbankingfinance.com/wealth/super-funds-eye-data-driven-customer-retention/
Super funds eye data-driven
Customer Retention
“They are very concerned
about members that are
leaving. They're trying to
focus on strategies to
stop them ... but the
predictive analytics
goes before that."
"They've got to start providing people
with a lot more information before
people push that button. Because so
many people do that – they always cash
in at the bottom or near bottom."
Stephen Mackley - Financial Synergy CEO

37. Source:
- http://www.kdnuggets.com/2016/03/combat-financial-fraud-using-big-data.html
- http://www.experian.co.uk/identity-and-fraud/fraud-statistics/?utm_source=direct&utm_medium=blog&utm_campaign=fraudreport14&result=success
Combat financial fraud with Data Science

38. Source:
- http://www.kdnuggets.com/2016/03/combat-financial-fraud-using-big-data.html
- http://www.experian.co.uk/identity-and-fraud/fraud-statistics/?utm_source=direct&utm_medium=blog&utm_campaign=fraudreport14&result=success
Combat financial fraud with Data Science

39. Source:
- http://www.kdnuggets.com/2016/03/combat-financial-fraud-using-big-data.html
- http://www.experian.co.uk/identity-and-fraud/fraud-statistics/?utm_source=direct&utm_medium=blog&utm_campaign=fraudreport14&result=success
Combat financial fraud with Data Science

40. Source:
- http://www.kdnuggets.com/2016/03/combat-financial-fraud-using-big-data.html
- http://www.experian.co.uk/identity-and-fraud/fraud-statistics/?utm_source=direct&utm_medium=blog&utm_campaign=fraudreport14&result=success
Combat financial fraud with Data Science

41. Source:
- http://www.kdnuggets.com/2016/03/combat-financial-fraud-using-big-data.html
- http://www.experian.co.uk/identity-and-fraud/fraud-statistics/?utm_source=direct&utm_medium=blog&utm_campaign=fraudreport14&result=success
Combat financial fraud with Data Science

42. Stock Investment Predictions
with Machine Learning
Fundamental
analysis
Technical
analysis
Data Mining
Technologies
Internet-based data
sources for stock
market prediction
Applications of
Complexity
Science for stock
market prediction
Source: https://en.wikipedia.org/wiki/Stock_market_prediction

43. Stock Investment Predictions
with Machine Learning
Source: https://en.wikipedia.org/wiki/Stock_market_prediction

45. Machine
Learning:
• Models manually
designed, statistical
significance
• Domain knowledge
vital

46. Machine
Learning:
• Models automatically
designed, predictions
on separate hold-
out/test data
• Domain knowledge
helpful

47. Building Models Manually is
No Longer a Feasible Option
🔧
The demand for predictive
models has outpaced the
supply of data scientists Unmet demand
for Data Science
💲HIGH COSTS
👤
SLOW,
COSTLY
INTEGRATION
FEWER
ACTIONS AND
INSIGHTS
💡💡💡💡💡
HIGH
TURNOVER

48. Moving forward, companies will
be valued not just on their big
data, but on the algorithms that
turn that data into actions and
impact customers.
“
”- Peter Sondergaard
SVP | Gartner Research

49. Maths &
Stats
Domain
Knowledge
Hacking
Skills
Data
Science

50. Time
Turns
business
analysts
into data
scientists
Makes data
scientists
more
productive
By accomplishing 2 goals:
DataRobot Fills the Data Science Skills Gap

51. Maths &
Stats
Domain
Knowledge
Hacking
Skills
Turns Business Analysts into Data Scientists
Business Analyst
provides the
domain
knowledge
DataRobot
takes care of
the math and
hacking

52. Maths &
Stats
Domain
Knowledge
Hacking
Skills
Makes Data Scientists More Productive
Do much
more with
little to no
coding
Expanded
modeling
toolkit

53. Differentiators
Speed & Accuracy
Unmatched levels of model automation
Captures knowledge, experience &
best practices of world’s top data
scientists
Accessibility
Users of all skillsets can build & deploy
predictive models
Intuitive GUI with built-in guardrails
allows users to build & deploy
predictive models

54. Changing the World with DataRobot
1 Data Scientist
10s of models
Team of Data Scientists
100s of models
Scales with more
people & more time
90,018,080 user models
100,000,000s of internal models
Scales with more CPU
resources & more data

56. HPE Apollo 2000 and Apollo 4200
Gen9 servers
HPE ProLiant DL Servers
Source: http://www.cloudera.com/partners/solutions/hewlett-packard-enterprise.html

57. HPE Apollo 2000 and Apollo 4200
Gen9 servers
HPE ProLiant DL Servers
Source: http://www.cloudera.com/partners/solutions/hewlett-packard-enterprise.html

58. eRic.Choo@ptcsys.com
To know more of what we do, come talk to us at
TABLEO u t s i d e t h e G r a n d B a l l r o o m