Uploaded byDaniel Katz
Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) Analytics - By Daniel Martin Katz + Michael J. Bommarito II
The document discusses the growing importance of machine learning as a service (#mlaas) in the legal analytics sector, highlighting its challenges and potential applications. It outlines the need for law organizations to develop a data strategy and human capital to leverage analytics effectively. The conclusion predicts an increase in legal tech and analytics, driven by #mlaas innovations.
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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 sourceand 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 wouldlike to look over the #LegalHorizon
- 3. We join ourstory already in progress… Part I< >
- 4. #DataScience are already influencing ourlives in a variety of meaningful ways #BigData #Analytics #A.I.
- 6. To date, themost successful commercial applications have massive returns to scale and aim for cross societal payoffs…
- 7.
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- 14. Given large fixedcosts
- 15. Given large fixedcosts infrastructure + human capital (data scientists)
- 16. harder to successfullydeploy high quality enterprise applications for relatively narrow (sub)verticals
- 18. The Rise of#LegalAnalytics Part II< >
- 19. Law is arelatively small vertical and there is lots of diversity among tasks lawyers undertake …
- 20. in addition there isa borderline pathological numerophobia among lawyers
- 21. Together with theimplicit (explicit) challenge of partnership as the dominant form of the organization within our sector
- 22. taken together thishas challenged the deployment of analytics in legal
- 23. Analytics / Quant LegalPrediction has come to law Notwithstanding these head winds—
- 24.
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- 31.
- 32. In a realsense, this represents just a narrow set of products
- 33.
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- 36. General Counsels asLegal 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.
- 38.
- 40. Lots of folksask me what is next in legal analytics …
- 41. A big partof the answer comes from one of the most dominant vectors in tech
- 42. both those inpositions of leadership and those in technical positions need to take stock
- 44. #MLaaS and the EnterpriseOpen Source Movement Part III< >
- 45. IBM WATSON First majoreffort at #MLaaS Machine Learning as a Service
- 46. IBM Watson isMLaaS and it would have purported to be among the biggest stories in tech over the past few years
- 47. Turns out thingswould layout in a slightly different fashion …
- 48. IBM Watson (perse) IBM Watson (as early #MLaaS) vs.
- 49. the democratization of machinelearning is underway
- 50. Emerging Business Model- Machine Learning as a Service #MLaaS
- 56.
- 57.
- 58. Machine Learning asa Service #MLaaS
- 59. Machine Learning asa Service #MLaaS
- 60.
- 61. Machine Learning asa Service #MLaaS
- 63. But wait thereis more …
- 64. Machine Learning asa Service #MLaaS
- 65. Machine Learning asa Service #MLaaS Enterprise Open Source Movement #OpenSource +
- 66. Enterprise Open SourceMovement #OpenSource
- 73.
- 75. Part IV< > TheLast Mile Problem and the New Dimension of Competition
- 76. historically one neededto build the full stack (i.e end to end) for an application
- 77. Standing on theShoulders of Giants
- 78. The (Emerging) LastMile Problem in (Legal) Analytics
- 79. Off the Shelf #MLaaS, etc. (perhapswith some configuration and/or customization) Unique Domain Specific Offering
- 80. MLaas + OpenSource Decreases Cost of Production Lowers the Cost of Protoyping
- 81. The New BallGame
- 82. Workflow Across the Machine LearningLandscape
- 83. Piece together the combinationsof #MLaaS + open source
- 84. to build enterpriseapplications which are unique combinations drawn from across the #MLaaS / open source spectrum
- 86.
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- 88. every organization inlaw needs a data strategy
- 89. Capture, Clean, RegularizeData to support a range of tasks
- 90. Deploy Data forSpecific Enterprise Applications Develop a data roadmap
- 91.
- 92. every organization inlaw needs a relevant human captial #LegalAnalytics
- 93. Opening the Human CapitalBottleneck
- 94. Probably going to needhomegrow your own talent
- 95. http://www.quantitativemethodsclass.com/Professor Daniel MartinKatz Intro Class
- 96. http://www.legalanalyticscourse.com/Professor Daniel MartinKatz Professor Michael J. Bommarito II Advanced Class
- 97.
- 98. First Wave vs. SecondWave Legal Tech
- 99. Second Movers can catchup faster …
- 100. Second Movers need lesscapital …
- 101. Second Movers who startnow will have lower fixed costs …
- 102. probably will not needto go for a series z round of funding
- 103.
- 104. Our Prediction on the#LegalHorizon
- 105. Our Prediction More LegalTech More Legal Analytics
- 106.
- 107. #MLaaS Machine Learning asa Service
- 108. Daniel Martin Katz @computational computationallegalstudies.com lexpredict.com danielmartinkatz.com illinois tech - chicago kent college of law@
- 109. Michael J. BommaritoII @ mjbommar computationallegalstudies.com lexpredict.com bommaritollc.com university of michigan center for the study of complex systems@