Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) Analytics - By Daniel Martin Katz + Michael J. Bommarito II

1. #MLaaS, open source and the future of (legal) analytics
Machine Learning
as a Service (#MLaaS)
daniel martin katz
blog | ComputationalLegalStudies.com
corp | LexPredict.com
page | DanielMartinKatz.com
michael j bommarito
blog | ComputationalLegalStudies.com
corp | LexPredict.com
page | bommaritollc.com
edu | chicago kent college of law edu | university of michigan cscs

2. Today I would like to look
over the #LegalHorizon

3. We join our story
already in progress…
Part I< >

4. #DataScience
are already influencing
our lives in a variety of
meaningful ways
#BigData #Analytics
#A.I.

6. To date, the most
successful commercial
applications have massive
returns to scale and aim
for cross societal payoffs…

7. Medicine

8. Finance

9. Logistics

10. Agriculture

11. Transportation

12. Retail

14. Given large fixed costs

15. Given large fixed costs
infrastructure
+
human capital
(data scientists)

16. harder to successfully deploy
high quality enterprise
applications for relatively
narrow (sub)verticals

18. The Rise of #LegalAnalytics
Part II< >

19. Law is a relatively small vertical
and there is lots of diversity
among tasks lawyers undertake …

20. in addition
there is a
borderline
pathological
numerophobia
among lawyers

21. Together with the implicit
(explicit) challenge of
partnership as the dominant
form of the organization
within our sector

22. taken together this has
challenged the deployment
of analytics in legal

23. Analytics /
Quant Legal Prediction
has come to law
Notwithstanding these head winds—

24. #LegalAnalytics
Quantitative Legal Prediction

25. #LegalAnalytics
Quantitative Legal Prediction

26. #LegalAnalytics
Quantitative Legal Prediction

27. #LegalAnalytics
Quantitative Legal Prediction

28. #LegalAnalytics
Quantitative Legal Prediction

29. #LegalAnalytics
Quantitative Legal Prediction

31. Some Commercial Applications

32. In a real sense, this represents
just a narrow set of products

33. #ContractAnalytics
Quantitative Legal Prediction

34. #JudicialAnalytics
Quantitative Legal Prediction

35. #PredictiveCoding #E-Discovery
Quantitative Legal Prediction

36. General Counsels as Legal
Procurement Specialists
TyMetrix/ELM -
Using $50 billion+ in Legal
Spend Data to Help GC’s
Look for Arbitrage
Opportunities, Value
Propositions in Hiring Law
Firms
#LegalSpendAnalytics
Quantitative Legal Prediction

37. #LegalAnalytics
Quantitative Legal Prediction
https://lexsemble.com/

38. #NegotiationAnalytics
Quantitative Legal Prediction

40. Lots of folks ask me what is
next in legal analytics …

41. A big part of the answer
comes from one of the most
dominant vectors in tech

42. both those in positions of
leadership and those in technical
positions need to take stock

44. #MLaaS and the
Enterprise Open
Source Movement
Part III< >

45. IBM WATSON
First major effort at #MLaaS
Machine Learning as a Service

46. IBM Watson is MLaaS and it
would have purported to be
among the biggest stories in
tech over the past few years

47. Turns out things would layout in
a slightly different fashion …

48. IBM Watson (per se)
IBM Watson (as early #MLaaS)
vs.

49. the democratization of
machine learning is underway

50. Emerging Business Model -
Machine Learning as a Service
#MLaaS

56. The
Cloud
Wars

57. Commercial Examples

58. Machine Learning as a Service
#MLaaS

59. Machine Learning as a Service
#MLaaS

60. Machine
Learning as
a Service
#MLaaS

61. Machine Learning as a Service
#MLaaS

63. But wait there is more …

64. Machine Learning as a Service
#MLaaS

65. Machine Learning as a Service
#MLaaS
Enterprise Open Source Movement
#OpenSource
+

66. Enterprise Open Source Movement
#OpenSource

73. https://techcrunch.com/2016/06/19/the-next-wave-in-software-is-open-adoption-software/

75. Part IV< >
The Last Mile
Problem and the
New Dimension of
Competition

76. historically one needed to
build the full stack (i.e end to
end) for an application

77. Standing on
the Shoulders of Giants

78. The (Emerging) Last Mile Problem
in (Legal) Analytics

79. Off the
Shelf
#MLaaS, etc.
(perhaps with some
configuration
and/or
customization)
Unique Domain
Specific Offering

80. MLaas + Open Source
Decreases Cost of Production
Lowers the Cost of Protoyping

81. The New Ball Game

82. Workflow Across
the Machine
Learning Landscape

83. Piece together the
combinations of
#MLaaS + open source

84. to build enterprise applications
which are unique combinations
drawn from across the
#MLaaS / open source spectrum

86. Three Implications for
#LegalAnalytics
#LegalTech
#LegalAI
Part V< >

87. Implication #1< >

88. every organization in law
needs a data strategy

89. Capture, Clean, Regularize Data
to support a range of tasks

90. Deploy Data for Specific
Enterprise Applications
Develop a
data roadmap

91. Implication #2< >

92. every organization in law
needs a relevant human captial
#LegalAnalytics

93. Opening the Human
Capital Bottleneck

94. Probably going to
need homegrow
your own talent

95. http://www.quantitativemethodsclass.com/Professor Daniel Martin Katz
Intro Class

96. http://www.legalanalyticscourse.com/Professor Daniel Martin Katz
Professor Michael J. Bommarito II Advanced Class

97. Implication #3< >

98. First Wave vs.
Second Wave
Legal Tech

99. Second Movers can
catch up faster …

100. Second Movers
need less capital …

101. Second Movers
who start now
will have lower
fixed costs …

102. probably will not
need to go for a
series z round of
funding

103. In Conclusion< >

104. Our Prediction
on the #LegalHorizon

105. Our Prediction
More Legal Tech
More Legal Analytics

106. Leveraging (in part) …

107. #MLaaS
Machine Learning as a Service

108. Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chicago kent college of law@

109. Michael J. Bommarito II
@ mjbommar
computationallegalstudies.com
lexpredict.com
bommaritollc.com
university of michigan center for the study of complex systems@