TWIMLcon 2019


Model as a Service for Real-Time Decisioning​

Case Study

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.

Session Speakers

Sr. Director, Software Engineering
Capital One
Software Engineering Manager
Capital One

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