Model as a Service for Real-time Decisioning

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 eff …

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.