Laura Schornack and Derek Ferguson walk you through the conception, design, and implementation of an end-to-end machine learning system for financial pattern recognition in the private cloud. Starting with just a basic theoretical knowledge of machine learning and containerization, you’ll walk away with a complete understanding of both the challenges in private cloud machine learning and the best solutions.
Laura and Derek begin by discussing the underlying machine learning concepts to be used in the solution. They describe the case—recognizing latent patterns in financial data—and introduce the technologies in the solution (TensorFlow, Kubernetes, and Kubeflow). Laura and Derek then walk you through the overall architecture of the solution, including primary stores and conduits of data into the system, backbone processing for training of the ML models, and execution capacity for predictions against these models. They conclude with a deep dive into the unique challenges around establishing a full ML solution in a private cloud.
Derek Ferguson is head of engineering for Chase’s Commercial Bank, where he is responsible for all aspects of DevOps, Agile transformation, and developer tooling. He graduated with honors from Chicago’s DePaul University and worked with a local ISP to deliver one of the world’s first commercial DSL deployments. Out of that effort, he was recruited into book writing and conference speaking, serving as editor in chief of a major software development magazine, authoring best-selling software development books such as Broadband Internet Access for Dummies, and speaking at events all over the world from JavaOne to Microsoft’s TechEd.
Laura Schornack is a JPMorgan Chase expert engineer and lead design architect for shared services. Previously, she worked for world-renowned organizations such as IBM and Nokia. She holds a degree in computer science from Laura of Illinois at Urbana-Champaign.
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