John Swift

Sr. Director, Software Engineering
Capital One

John grew up in California, his home town is San Diego, where he spent his formative years in high school and college. He’s  worked in software engineering and architecture since the late 80s and has had the opportunity to work at a number of great technology firms over the years. Prior to coming to Capital One, John was the Chief Architect for RSA Security, however, other notable companies include Qualcomm, MCI, WorldCom, UUNET, Sun Microsystems, AOL, Yahoo, BMC Software, DELL / EMC, RSA Security, Verisign, Adobe, a few successful startups as well, a number of government contracting firms, and is now at Capital One. John enjoys this industry a great deal and loves working with brilliant driven people who want to make a difference and deliver value every day. His hobbies include reading, movies, binge watching TV shows, enjoys traveling and fine wines as well as gourmet foods and sharing all those things with friends and family.

Tuesday, October 1
11:50 am
12:15 pm
Robertson 1
Hosting models and productionizing them is a pain point. Let’s fix that. Imagine a stream processing platform that leverages ML models and requires real-time decisions. While most solutions provide tightly coupled ML models in the use case, these may not offer the most efficient way for a data scientist to update or roll back a model. With model as a service, disrupting the flow and relying on technical engineering teams to deploy, test, and promote their models is a thing of the past. It’s time to focus on building a decoupled service-based architecture while upholding engineering best practices and delivering gains in terms of model management and deployment. Other benefits also include empowering data scientists by supporting patterns such as A/B testing, multi-armed bandits, and ensemble modeling. Sumit and John demonstrate their work with a reference architecture implementation for building the set of microservices and lay down, step by step the critical aspects of building a well-managed ML model deployment flow pipeline that requires validation, versioning, auditing, and model risk governance. They discuss the benefits of breaking the barriers of a monolithic ML use case by using a service-based approach consisting of features, models, and rules. Join in to gain insights into the technology behind the scenes that accepts models built using popular libraries like H2O, Scikit-learn, or TensorFlow and serve them via REST/gRPC which makes it easy for the models to integrate into business applications and services that need predictions.
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