Machine Learning: Build, Deploy, Monitor at Scale
How do you deploy and manage not just 1, but 100’s of machine learning models? What changes when you move from batch processing to synchronous calls? When your algorithms become mission critical, what changes? What is seemingly easy when working for a small company, with lower volumes or with batch operations becomes exponentially more difficult at scale. Off the shelf development and deployment platforms have existed for years, but use cases are becoming broader and corporations are starting to customize and invest internally. In this presentation, I will discuss the infrastructure and processes necessary to operationalize data science at scale.
Tom is an experienced data scientist specializing in development and deployment of algorithms into real time, mission critical, applications. Through the years, his work has ranged from ground up infrastructure buildouts in smaller startups to more mature large corporate America installations. He currently leads the data science team at eBay responsible for payments and risk, specializing in fraud detection and otherwise creating a safe platform for buyers and sellers. Previous roles included risk assessment and customer acquisition related to automated, high risk, online lending. Outside of work he spends most of his time on two wheels, racing mountain biking around central Texas.