While investment in infrastructure for AI continues to grow, attaining meaningful business results with applied machine learning and AI requires a practical roadmap that involves team structures and process design in addition to new technology adoption.
Bethann Noble, Abhishek Kodi, and Daniel Huss share their experience and best practices for designing and executing on a roadmap for open data science and AI for business. Bethann, Abhishek, and Daniel discuss State Street’s migration from legacy platforms to open source data science software such as Python, R, and Apache Spark and outline new org structures and practices for rapidly delivering new AI products to market, including State Street’s Verus, a machine learning-based insight service for investors.
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Bethann Noble is a director of product marketing at Cloudera, responsible for driving marketing and strategy initiatives in support of Cloudera machine learning solutions. Previously, Bethann held roles in developer and product marketing, technical sales, and software engineering at IBM, with several years’ experience in high-performance computing and big data and analytics technologies. She holds a bachelor’s degree in mathematics from the University of Texas at Austin.
Daniel Huss is head of product management for Verus at State Street. Previously, he laid the foundation for the very product he’s building now with Boston Consulting Group’s Digital Ventures. In another life, he would have been a physicist. He tries to fake it by applying as much scientific method as he can to product development and entrepreneurship, valuing uncertainty and ambiguity and having a default setting of “build, measure, learn.”
Abhishek Kodi is a data engineer on the Verus team at State Street, where he strives to create novel solutions for deterministic and nondeterministic problems that balance business asks against learning frameworks. His focus area is supervised and unsupervised analytical solutions that merge natural language processing, supply chain, and financial data. His integral approach to understanding business needs, playing a part in designing ML-driven solutions, and implementing them for production systems enables sustainable pathways from ML tools to meaningful business solutions.
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