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.
