Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) Analytics – By Daniel Martin Katz + Michael J. Bommarito II from Daniel Katz
- How Machine Learning is already influencing our lives
- The Rise of Legal Analytics
- Machine-Learning-as-a-Service (MLaaS) and Enterprise Open Source
- The Last Mile Problem and New Dimension of Competition
- Machine learning has been most successful in applications where scale is attainable, like logistics and transportation.
- Cultural mindset and investment incentives have created headwinds for legal analytics.
- Contracts, spend, and ADR are three exemplary areas where analytics have been deployed successfully in legal.
- The democratization of machine learning is underway.
- The most sophisticated technology firms are increasingly releasing their machine learning IP under open source models.
- The cost of prototyping or developing a solution for a narrow vertical is dramatically lower using MLaaS and open source.
LexPredict is an enterprise legal technology and consulting firm, specializing in the application of best-in-class processes and technologies from the technology, financial services, and logistics industries to the practice of law, compliance, insurance, and risk management. In this deck, we outline our views on how machine learning as a service (MLaaS) and open source will help shape the future of legal analytics by lowering the cost of the last mile and prototyping.