Machine Learning in Business: Data science has been one of the fastest growing jobs of the past 10 years and companies are rapidly integrating it into their businesses. In this talk I will discuss the practical skills and techniques needed to successfully integrate data science into a business, as well as some common struggles and pitfalls that commonly occur.

1. Practical
Machine
Learning
for
Business
Michael
Galvin
galvin.mj@gmail.com
Twitter:
@MikeJGalvin

2. Successful
Machine
Learning
in
Business
• Relationships
and
Education
• Reputation
and
Buy
In
• Data
Science
• Preparing
• Modeling
and
Implementation
• Monitoring
• Necessary
Skills

3. Relationships
and
Education

6. Bridging
the
Gap

7. Communication

8. Science
Experiment

9. Unicorn

10. Education

13. Business
Needs
User
Value

14. Current
Capabilities

15. Current
Capabilities
Where
are
we?

16. Current
Capabilities
Where
are
we?
Where
do
we
want
to
be?

17. Reputation
and
Buy
In

19. Quick
Wins

20. Quick Wins
Show
you
can
produce
something

21. Quick
Wins
Show
what
you
did
was
valuable

22. Data
Science
Preparing
Modeling
and
Implementation
Monitoring

23. Specific
Problem
Data

24. Specific
Problem
Data

25. Specific
Problem
Data

26. What
problem
are
we
trying
to
solve?

27. Success

28. Success
What
does
it
look
like?

29. Expectations

30. Expectations
Feasibility

31. Expectations
Feasibility
Timeline

32. Expectations
Feasibility
Timeline
Issues

33. Data
Science
Preparing
Modeling
and
Implementation
Monitoring

34. Don’t
Over
Complicate

35. It’s
Not
Just
Algorithms
Algorithms Data Assessment Result

36. Learn
About
the
Data
Algorithms Data Assessment Result

37. Learn
About
the
Data
Important?
Algorithms Data Assessment Result

38. Learn
About
the
Data
Important?
Features
Algorithms Data Assessment Result

39. Validation
Strategy
Algorithms Data Assessment Result

40. Validation
Strategy
Model
Assessment
Algorithms Data Assessment Result

41. Validation
Strategy
Model
Assessment
More
Data
isn’t
Always
Better
Algorithms Data Assessment Result

42. Get
Something
Working
Algorithms Data Assessment Result

43. Get
Something
Working
Deploying
Algorithms Data Assessment Result

44. Get
Something
Working
Deploying
Improve,
Iterate
Algorithms Data Assessment Result

45. Are
People
Using
Your
Solution?
Valuable
Accessible

46. ML
Metric Business
Metric

47. Pipelines

50. Pipelines
• Scalable
• Reproducible
• Etc.

51. Data
Science
Preparing
Modeling
and
Implementation
Monitoring

52. Monitoring

53. Is
is
working?
Feedback,
Iterate
Model
Comparison

55. Work
Together,
Human
in
the
Loop

56. Necessary
Skills

57. Grit
Problem
Solving
Learn
to
Learn
Communicate
Collaborate
Passion
It’s
takes
more
than
technical
skills

58. You’re
In
A
Good
Position!

59. You’re
In
A
Good
Position!
Influential

60. You’re
In
A
Good
Position!
Influential
Impactful

61. You’re
In
A
Good
Position!
Influential
Impactful
Drive
Change

62. Thank
You
Michael
Galvin
galvin.mj@gmail.com
Twitter:
@MikeJGalvin