It's an exciting time to be thinking about artificial intelligence and machine learning, but it can be challenging to figure out a practical and reasonable approach to using these technologies. In this webinar, we'll discuss the history of machine learning, why now is the time to think about investing, and how to identify and execute on practical machine learning opportunities.

1. DEMYSTIFYING ML & AI
Hilary Mason, GM of Machine Learning

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WELCOME!
• Review the current state of ML
(Machine Learning) and AI (Artificial
Intelligence) capabilities
• Strategic advice for effectively
executing
• Historical context helpful to understand
current capabilities
• Maybe a cat or two?

4. In 2018, the world
is becoming
computable.

5. [AI hype article]

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AI
MACHINE
LEARNING
DATA SCIENCE
ANALYTICS
BIG DATA

9. “big data”
Analytics
& BI
Product data
science R&D
data

10. Where did machine learning come from?

11. Where did machine learning come from?

12. ML is fundamentally about learning a
function from data.

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FRANK
ROSENBLATT,
~1960

14. The Perceptron

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IN 1969, I WENT TO THE PROVOST, PROF. CHARLES J.
MORROW, AND I SAID, "WE ARE READY AND HAVE THE
NECESSARIES TO BUILD AN INTELLIGENCE AMPLIFYING
SYSTEM IN WHICH THE MIND AND MACHINE WILL
COOPERATE ON TACKLING HARD PROBLEMS. WHO ON THIS
FACULTY MOST DESERVES TO HAVE HIS INTELLIGENCE
AMPLIFIED?”
Fred Brooks

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TODAY, OUR POWERS HAVE GROWN
Three major changes make ML possible.
ALGORITHMS COMPUTE DATA!

17. These are still just computer programs!
We are not talking about general
intelligence.

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CLOUDERA FAST FORWARD LABS RESEARCH
Applied machine learning research reports and prototypes

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LOOKING AHEAD
IDENTIFYING WHAT’S COMING NEXT
Making the recently possible, useful.
ECONOMIC
CONSTRAINTS AND
COMMODITIZATION OF
TOOLING
RESEARCH ACTIVITY DATA AVAILABILITY AND
UTILITY

20. The Perceptron

24. Rich media is now perceptible.
Example use cases:
• Quality control systems.
• Surgical robotics realtime error
detection.
• Real world navigation in constrained
environments.
• Legacy media made useful and
searchable.

25. The Perceptron

28. Language is becoming computable.
Example use cases:
• Insurance company understanding
personas of applicants for marketing.
• Bank parsing customer service call
transcripts to better recommend
actions.
• Investment bank automatically parsing
the news effectively for commodities
traders.

29. The Perceptron

32. We must understand what black boxes do!
Example use cases:
• Regulatory compliance and bias
testing
• Telecom churn reasoning
• Reverse engineering 3rd party models

33. What makes a great data product?

35. The biggest opportunities
are in the enterprise.

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IDENTIFYING OPPORTUNITY
It’s at the intersection of…
Your Data
+
Your Business
+
Technical Capabilities

37. We are starting to see significant value
being generated with data and machine
learning.

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WHAT DOES SUCCESS LOOK LIKE?
It’s surprisingly hard to distinguish from mediocrity.
1. The right priorities
2. The right organizational structure
3. The right technology

39. How to develop a strong data strategy
in three easy steps!:

40. 0) Drink coffee. Have ideas.
(have bad ideas, too.)

41. 1) Select a set of investments.
Valuetobusiness
Time to develop

42. This is not wrong.
(it’s incomplete)

43. 2) Each investment should
enable future investments.

44. 3) Not all investments will pay
out, use a portfolio approach.

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BUILDING A FUNCTIONAL DATA ORGANIZATION
This is the key aspect of functional data management.
1. You cannot outsource the data capability.
2. Data science requires data scientists!
3. Every organizational structure is a compromise.

46. There is a generic
formulation of your problem.
Then there’s your
problem.
This is the data product gap.

47. Data science is a new
professional role.

49. Executives must embrace
data, too.

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DATA SCIENCE IS A
TEAM SPORT.
This talent doesn’t
come from the
same places as
traditional
engineering talent.

51. Two main models of team structure:
Centralized vs Distributed
Hint: there is no one right answer.

52. What makes a great enterprise data product?
Please consider some examples…

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CHECKLIST
AUTOMATION AND
US FEDERAL TAX
CODE

54. First, invest in automation to support
an existing process.
Then, invest in scaling to allow entirely
new products and applications.

55. Consider augmenting vs
replacing humans.

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CALL CENTER
OPTIMIZATION
Getting the right
information to the
right person to
make the best
decision.

57. Can a human do it?
Can a group of humans do it
reliably?

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IDENTIFYING OPPORTUNITY AND EXECUTING EFFECTIVELY
It’s at the intersection of…
Your Data
+
Your Business
+
Technical Capabilities
Priorities
+
Functional Organization
+
Technology

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MACHINE LEARNING AT CLOUDERA
OUR PHILOSOPHY
• We empower our customers to create and
own their digital transformation
• We offer an open platform that can
support workloads running anywhere
• We accelerate enterprise-grade data
science

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RESEARCH SOFTWARESERVICES
OUR APPROACH, TODAY – ALL ON THE CLOUDERA PLATFORM
The Fast Forward Labs research
subscription shares emerging
capabilities in data science and
machine learning, and
accelerates your ability to identify
and execute on new use cases
Our services cover strategic
opportunities and advising as
well as technical use case
exploration
Cloudera Data Science
Workbench is a software
platform that accelerates data
science from exploration to
production

62. THANK YOU