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 deliver gains for model operationalization.
Sumit demonstrates a reference architecture implementation for building the set of microservices and lay down, the critical aspects of building a well-managed ML model deployment flow pipeline that requires validation, versioning, auditing, and model risk governance. See the benefits of breaking the barriers of a monolithic ML use case by using a service-based approach consisting of features, models, and rules.
Ravi is a Lead Software Engineer, Team Lead/Architect and Director at Capital One specializing in Decision Processing, Platform Delivery, and Cloud Engineering.
Manager/ Architect Software Engineering, Capital One
Sumit Daryani is a software engineering manager and architect at Capital One. He works on a real-time machine learning decision platform to protect its banking platform and foster quick decisions to support the fraud strategy. Previously, Sumit was a full-stack engineer on a diverse... Read More →